Method and apparatus for hemodynamically characterizing a neurological or fitness state by dynamic light scattering (DLS)

ABSTRACT

A method and apparatus for hemodynamically characterizing a neurological or fitness state by dynamic scattering light (DLS) is disclosed herein. In particular, a non-pulsatile blood-shear-rate-descriptive (BSRD) signal(s) is optically generated and analyzed. In some embodiments, the BSRD signal is generated dynamically so as to adaptively maximize (i.e. according to a bandpass or frequency-selection profile) a prominence of a predetermined non-pulsatile physiological signal within the BSRD. In some embodiments, the BSRD is subjected to a stochastic or stationary-status analysis. Alternatively or additionally, the neurological or fitness state may be computed from multiple BSRDs, including two or more of: (i) a [sub-200 Hz, ˜300 Hz] BSRD signal; (ii) a [˜300 Hz, ˜1000 Hz] signal; (iii) a [˜1000 Hz, ˜4000 Hz] signal and (iv) a [˜4000 Hz, z Hz] (z&gt;=7,000) signal.

BACKGROUND

Detecting Emotion-State/Stress-State/Mood-State

There is an ongoing need for detecting emotional state, stress-states,and mood-states of warm-blooded (e.g. mammals or birds) individuals.Prior art techniques for detecting an emotional state are based onanalyzing facial expressions, voice intonations, text generated by thesubject, eye movement, pulse and blood pressure variability.

Thus, it has been known for many years that as a subject gets nervous orexcited or scared, his/her pulse-rate (HR) tends to temporarily increase(i.e. relative to a baseline pulse) until the stress-state or mood-statepasses. Thus, it is possible to detect a person's stress-state ormood-state using any pulse-meter or ECG device. Examples of devices fornon-invasive optical measuring pulse include pulse oximeters and dynamiclight scattering (DLS) devices disclosed in WO 2008/053474 andWO2012064326, each of which is incorporated herein by reference in itsentirety.

Based on the time variations pattern of HR, which is call Heart RateVariability (HRV), some of the manifestations of the Central NervousSystem (CNS) are subject to algorithmic representations. Specifically,the responses of sympathetic and parasympathetic nervous system arereflected in HRV. Naturally, any expressions of stress or emotions alsoaffect the HRV pattern. Unfortunately, HRV by itself tends to be oflimited reliability, specificity and accuracy. For example, due tonatural variations, ‘baseline’ pulse rate and HRV may be difficult orimpossible to accurately gauge. In addition, there may be many ‘falsepositive’ situations where a user's pulse increases for reasons (e.g.the user is climbing up the stairs) other than being in an excitedemotional state,

In addition, HR and HRV are of at most limited utility when attemptingto differentiate between different types of ‘excited’ states—i.e.between fearful and angry. In addition in order to extract HRVparameters reliably, at least 5 minutes of the measurement are required.This fact may impose additional limitations for consumer applications ofHRV.

Detecting Fitness Parameters

There is an ongoing need for non-invasive and continuous methods (andrelated apparatus) for detecting a ‘fitness-parameter’ of an individual.Thus, a low fitness' score may be indicative of an elevated risk ofcardio-vascular disease or reduced capability to withstand a load.Unfortunately, preventative care is often neglected, for example, due tothe high cost (or inconvenience) of physician visits.

HR and HRV are also can provide some indications for cardio-vascularstatus however, since the same parameters respond to stress andemotional stages, they lack of specificity.

Once again, HR and HRV are useful as rough indicators but, bythemselves, are not robust enough for consistently obtaining accuratereadings for many subjects.

Pulsatile and Non-Pulsatile Blood

FIG. 1A schematically illustrates blood flow within a blood vessel. Asillustrated, at the vessel walls due to the ‘no-slip’ boundarycondition, blood velocity (and hence shear rate) drops off to zero.

When any pressure gradient is applied to a vessel (i.e. in the case ofblood vessels, this is a ‘pulsatile pressure-wave’), fluid within thevessel flows according to the pressure gradient. However, at locationsnear the blood vessel wall, temporal fluctuations in velocity (andshear) are not strongly correlated to those of the pulse-wave (e.g.because the blood vessel wall is not perfectly rigid). Similarly, theremay be at most very weak correlation between the blood flow andpulsatile components in the very small vessels (capillary vessels),where the size of the moving particles (i.e. red blood cells (RBCs)) arecomparable to the blood diameter.

As shown in FIG. 1A, in arterial vessels, at locations closer to thecenter-line the blood may be regarded as ‘pulsatile blood’ (i.e. due toits flow properties) where temporal fluctuations in velocity (and shearrate) are strongly correlated with the pulsatile pulse-pressure wave. Incontrast, there is little or no correlation between temporalfluctuations of shear rate and those of the pulse-pressure wave atlocations near the blood vessel wall. Thus, in FIG. 1A, blood near thewall is labeled as ‘non-pulsatile blood.’ The terms ‘pulsatile blood’and ‘non-pulsatile blood’ refer to flow properties of the blood—i.e. howthe blood is flowing in vivo.

Referring to FIG. 1B, it is noted that blood flow in arteries is mostlypulsatile, while in many other blood vessels (e.g. capillaries), theopposite is true.

Modulation of Blood Flow (and Blood Shear) by Physiological Signals

Reference is now made to FIG. 2.

As noted in the article “Physics of the Human Cardiovascular System” byStefanovska and Bracic¹, in addition to the HRV, the CNS affects the“blood flow variability”. The CNS governs multiple physiologicalprocesses which can be expressed in terms of peripheral bloodoscillations in different frequency bands. ¹Physics of the humancardiovascular system by Aneta Stefanovska and Maja Bracic, ContemporaryPhysics, 1999, volume 40, number 1, pages 31-55

Listed in FIG. 2 (adapted from FIG. 10 of Stefanovska and Bracic) arethe following categories of physiological processes, each of whichfunctions as an ‘oscillator’ of the blood flow signal in a respectivefrequency band: (i) metabolic process(es) which modulate blood flowsignal in the frequency band [˜0.01 Hz, ˜0.02 Hz]; (ii) neurogenicprocess(es) which modulate blood flow signal in the frequency band[˜0.02 Hz, ˜0.06 Hz]; (iii) myogenic process(es) which modulate bloodflow signal in the frequency band [˜0.06 Hz, ˜0.15 Hz]; (iv) respiratoryprocess(es) which modulate blood flow signal in the frequency band[˜0.15 Hz, ˜0.5 Hz]; and (v) heart/pulsatile process(es) which modulateblood flow signal in the frequency band [˜0.4 Hz, ˜2 Hz].

The aforementioned frequency bands are for humans—for other mammals, thefrequency values may differ.

Physiological processes of each category of processes modulate theblood-flow (and also the blood-shear) ‘signal.’ Because blood flow (andblood-shear) signal may be considered a combination of multiple signals,physiological processes may be said to generate a ‘physiologicalresponse signal’ present within the blood-flow (and also the blood-shearsignal)—each physiological signal may be said to ‘contribute’ to theoverall blood-flow oscillation pattern (and also the blood-shearsignal). For the present disclosure, the term ‘response signal’therefore relates to the response(s) to input and/or feedback from thecentral nervous system as manifested within blood flow.

One example of a physiological response signal is a Mayer wave.

It is noted that the heart/pulsatile signal is the well-known ‘pulsatilesignal’—the pumping of blood by the heart directly influences the bloodflow (and blood-shear) response signal in many locations within thecirculatory system. In contrast, the ‘respiratory signal’ from the‘respiratory processes/oscillators’ is not merely the ‘breathingpattern’—instead, this refers to the indirect influence of respirationupon blood flow.

One example of a myogenic process is Mayer waves. According toWikipedia:

Mayer waves are cyclic changes or waves in arterial blood pressurebrought about by oscillations in baroreceptor and chemoreceptor reflexcontrol systems. The waves are seen both in the ECG and in continuousblood pressure curves and have a frequency about 0.1 Hz (10-secondwaves). These waves were originally described by Siegmund Mayer, EwaldHering and Ludwig Traube hence originally called “Traube-Hering-Mayerwaves”.

Mayer waves can be defined as arterial blood pressure (AP) oscillationsat frequencies slower than respiratory frequency and which show thestrongest, significant coherence (strength of linear coupling betweenfluctuations of two variables in the frequency domain) with efferentsympathetic nervous activity (SNA). In humans, AP oscillations whichmeet these properties have a characteristic frequency of approx. 0.1 Hz;0.3 Hz in rabbits and 0.4 Hz in rats.

The hemodynamic basis of Mayer waves are oscillations of the sympatheticvasomotor tone of arterial blood vessels, because Mayer waves areabolished or at least strongly attenuated by pharmacological blockade ofalpha-adrenoreceptors. Within a given biological species, theirfrequency is fairly stable; in humans it has been shown that thisfrequency does not depend on gender, age or posture. It has beensuggested that Mayer waves trigger the liberation of endothelium-derivednitric oxide (NO) by cyclic changes of vascular shear stress which couldbe beneficial to end organ functioning.

Mayer waves are correlated with heart rate variability.

Takalo et al. (1999) state that “the frequency shift of Mayer waves tolower frequencies is associated with an increased risk of developingestablished hypertension.”

In pulsatile blood, the heart/pulsatile shear signal is characterized bywell-known features, illustrated in FIG. 2B. In arterial component ofthe flow, there may be signals other than the pulsatile signal—however,typically the energy of the pulsatile signal dominates that of the otherphysiological signals.

Dynamic Light Scattering (DLS) for Non-Invasive In-Vivo Measurement ofBiological Parameters

WO 2008/053474 and WO2012064326, each of which are incorporated hereinby reference in its entirety, each disclose a system and method for invivo measurement of biological parameters by dynamic light scatteringtechniques.

In particular, WO 2008/053474 discloses a novel optical techniquesuitable for the in vivo measurement in a subject utilizing dynamiclight scattering (DLS) approach. The effect of DLS are utilized for themeasurement of variety of blood related parameters, such as viscosity ofthe blood and blood plasma, blood flow, arterial blood pressure andother blood chemistry and rheology related parameters. DLS is awell-established technique to provide data on the size and shape ofparticles from temporal speckle analysis. When a coherent light beam(laser beam, for example) is incident on a scattering (rough) surface, atime-dependent fluctuation in the scattering property of the surface andthus in the scattering intensity (transmission and/or reflection) fromthe surface is observed. These fluctuations are due to the fact that theparticles are undergoing Brownian or regular flow motion as a result ofnon-uniform blood flow (i.e. manifested in blood-shear) and so thedistance between the particles is constantly changing with time. Thisscattered light then undergoes either constructive or destructiveinterference by the surrounding particles and within this intensityfluctuation information is contained about the time scale of movement ofthe particles. The scattered light is in the form of speckles pattern,being detected in the far diffraction zone. The detected signal isamplified and digitized for further analysis by using theautocorrelation function (ACF) technique. The technique is applicableeither by heterodyne or by a homodyne DLS setup.

The kinetics of optical manifestations of two kinds of physiologicalsignals is measured in vivo: the pulsatile signal associated with heartbeats and the post-occlusion optical signal which is induced by anartificially generated blood flow cessation. The light transmissionand/or reflection signals are used as a control of the physiologicalresponse. This kind of control measurement can be carried outsimultaneously with the DLS reflection measurement. The mutualcorrespondence between DLS and standard optical signals is subject to acomparison analysis.

Reference is now made to FIGS. 3A-3B. FIG. 3A, taken from WO 2008/053474(and slightly modified) illustrates an apparatus for performing a DLSmeasurement. A coherent light source (e.g. a vertical-cavitysurface-emitting laser (VCSEL)) emits coherent light to illuminate theskin (step S201)—this coherent light scatters off of red blood cells(RBCs) within blood vessels of the skin (or beneath the skin) to inducea scattered-light optical response The optical response is detected(step S205) by photodetectors to generate an electrical signaldescriptive of the scattered-light optical response (see FIG. 4 of WO2008/053474). Scattered-light-optical-response-descriptive electricalsignal (i.e. one example is in FIG. 4 of WO 2008/053474; another exampleis the signal A(t) passed from analog electronics assembly 270 todigitizer 204 of the FIG. 2 of WO2012064326)) is processed (e.g. usingautocorrelation or power spectrum analysis) (step S213) to produce atime-dependent blood-shear-rate descriptive signal or BSRD. Examples ofa BSRD are illustrated in FIGS. 9-13 of WO 2008/053474). One or morephysiological parameter(s) (e.g. pulse rate or blood pressure) arecomputed from the BSRD signal.

It is noted that red blood cells (RBCs) suspended within blood plasma donot travel at the same velocity—instead, there is a velocitydistribution. The BSRD signal describes differences in velocities ofred-blood-cells suspended in the blood plasma. In certain frequencydomains, blood-shear is primarily due to pulse. By illuminating skin,collecting scattered light and subjecting the scattered light to speckleanalysis (e.g. to analyze temporal fluctuations of speckle patterns), itis possible to derive a signal descriptive of a blood-shear over a crosssection of blood vessel(s) and/or over a ensemble of blood vessels.

FIG. 4, taken from WO 2008/053474, illustrates one example of ascattered-light time-dependent optical response signal generated in stepS205.

FIG. 5A illustrates one example of a time-dependent blood-shear-ratedescriptive signal. FIG. 5B illustrates another example of atime-dependent blood-shear-rate descriptive signal.

Although both the signals of FIG. 5A-5B are optically and electronicallygenerated according to steps S201-213 of FIG. 3B, there is a fundamentaldifference—the example of FIG. 5A is generated by computing a powerspectrum integral of the optical response signal (i.e. generated in stepS205) over the frequency interval [2700 Hz, 10,000 Hz] while the exampleof FIG. 5B is generated by computing a power spectrum integral of theoptical response signal (i.e. generated in step S205) over the frequencyinterval [0 Hz, 548 Hz].

Inspection of FIGS. 5A and 5B shows that the blood-shear-ratedescriptive signal follows that of a pulse-wave, while that of FIG. 5Bdoes not. Thus, the BSRD (shear-rate-descriptive) signal of FIG. 5A is‘pulsatile’ while that of FIG. 5B is not. The DLS spectral response willbe a superposition of responses of different components across the bloodvessels radius, according to the shear rate at each specific point.

When generating the signal of FIG. 5A the frequency-selection profile[2700 Hz, 10000 Hz] is employed in step S213 and the result is thepulsatile signal illustrated in FIG. 5A. In contrast, when generatingthe signal of FIG. 5B the frequency-selection profile [0 Hz, 548 Hz] isemployed in step S213 and the result is the non-pulsatile signalillustrated in FIG. 5B.

FIG. 6A is a flow diagram for the device and method of WO 2008/053474.The illumination signal of FIG. 6A is generated in step S201 of FIG. 3B.Light of the illumination signal is reflected and/or transmitted and/ordeflected by red blood cells within an ‘ensemble’ of blood vessels'(illustrated in FIG. 6A) to module the signal into a light responsesignal. The light response signal is received by photodetector(s) instep S205 of FIG. 3B to generate the response-descriptive electricalsignal (illustrated in FIG. 6A).

FIG. 6B relates to the method and device of WO2012064326 where first andsecond photodetectors (e.g. at respective locations Loc_1, Loc_2)respectively receive the light response signal to generate respectiveresponse-descriptive electrical signals specific to the respectivelocations Loc_1, Loc_2). These signals are processed by analogelectronic circuitry to generate yet another response-descriptiveelectrical signal.

As discussed in WO2012064326, there are number of differences between(i) the ‘input’ response-descriptive electrical signals (i.e. ‘first’and ‘second’ signals) that are input to the subtraction analog circuitryand (ii) the output response-descriptive electrical signals—e.g. the ACcomponent of the output signal has a much stronger contribution than inthe input signals, the stochastic component of the output signal has amuch stronger contribution than in the input signals.

FIG. 7A illustrates an example of a configuration for performing themethod of FIG. 3B-FIG. 7B is one example of the subtraction analogcircuitry of FIG. 6B.

The output of FIGS. 6A, 6B is responsive descriptive electrical signal.FIG. 8 relate to both WO 2008/053474 and WO2012064326 and is a data-flowdiagram describing the processing of any response-descriptive electricalsignal (e.g. that of FIG. 6A or the any response-descriptive electricalsignal of FIG. 6B) is processed. Thus, BSRD signal is generated by a‘BSRD” generator 190 (e.g. implemented in any combination of analogand/or digital hardware and/or software) according to a pulsatilefrequency-selection profile—e.g the profile [2700 Hz, 10000 Hz] of FIG.5A. A pulsatile frequency-selection profile generates a ‘pulsatile’ BSRDfrom a response-descriptive electrical signal. This pulsatile signal isthen analyzed by a second signal processor 194 (i.e. once again,implemented in any combination of analog and/or digital hardware and/orsoftware) which analyzes features of the pulse-wave within the BSRD—e.g.to generate a pulse signal or a heart rate variability signal.

Measuring and/or Classifying Stress, Stress Resistance and Mood

Wikipedia defines stress as follows:

-   -   Physiological or biological stress is an organism's response to        a stressor such as an environmental condition or a stimulus.        Stress is a body's method of reacting to a challenge. According        to the stressful event, the body's way to respond to stress is        by sympathetic nervous system activation which results in the        fight-or-flight response. Because the body cannot keep this        state for long periods of time, the parasympathetic system        returns the body's physiological conditions to normal        (homeostasis). In humans, stress typically describes a negative        condition or a positive condition that can have an impact on a        person's mental and physical well-being.

Stress may be categorized as (i) physical stress or (ii) non-physicalstress—e.g. mental stress or emotional stress. Physical stress may bebrought about by the subject's exerting him/herself. Non-physical stressincludes emotional stress and mental stress. For example, unpleasantnoises, unhappy thoughts, unpleasant visual images may trigger emotionalstress—unpleasant noises, thoughts and visual images are examples of‘stressors’. Mental exertion such as attempting to solve difficultarithmetic or to resolve cognitive interference (e.g. taking a ‘Strooptest’ named after John Ridley Stroop, author of the 1935 articleentitled “Studies of interference in serial verbal reactions”). Thus,mental exertion is a stressor that induces mental stress.

Stress may also be distinguished from ‘stress resistance’ which is afunction of temperament. Thus, there are some individuals where even aslight ‘stressor’ (e.g. an unpleasant noise at a very low volume)induces significant stress—they may be considered ‘high-strung’ or‘stress prone.’ In contrast, other individuals may exhibit a strongerresistance to stress, and may require a more significant ‘stressor’ inorder to exhibit a stress-state. Some individuals may exhibit a strongresistance to certain types of stress and much less resistance to othertypes of stress.

Pulsatile Measurement of Stress, Mood and Related Parameters

It is known in the art to employ heart-rate (or derived parameters suchas heart rate variability—HRV) as a ‘classifier’ for detecting emotionalepisodes or stress episodes. For example, if a person is very excited orangry, his/her pulse rate will increase relative to a ‘baseline.’

However, it is also known that heart rate by itself can be a ‘poorclassifier’ (i.e. by itself) for (i) detecting emotional or stressepisodes and/or (ii) for classifying emotional or stress episodes. In afirst example, a person's heart rate and HRV may increase for any numberof reasons, including but not limited natural variations, performance ofphysical exercise and weather conditions. Thus, in this example relyingexclusively on heart-rate may lead to a large number of ‘falsepositives’ erroneously indicating an emotional or stress episode.

In another example, both anger and intense happiness/excitement mayincrease a subject's heart-rate. Simply relying on elevated heart rateis inadequate for differentiating between two different types ofemotional/mood episodes (i.e. anger and intense happiness).

Although pulsatile-derived classifiers may certainly be useful, and havethere place, there is a need for accurate non-pulsatile techniques fordetecting stress or emotional episodes.

SUMMARY

Embodiments of the present invention relate to a method and apparatusfor hemodynamically characterizing a neurological or fitness state bydynamic light scattering (DLS)—e.g. by measuring fluctuations (i.e.shear of) in skin blood flow.

In contrast with previously-disclosed DLS-based techniques that rely onanalyzing a pulsatile signal (or that are limited to occluded blood),presently-disclosed techniques relate to computing a non-pulsatileblood-shear-rate-descriptive (BSRD) signal(s).

A method and apparatus for hemodynamically characterizing a neurologicalor fitness state by dynamic scattering light (DLS) is disclosed herein.In particular, a non-pulsatile blood-shear-rate-descriptive (BSRD)signal(s) is optically generated and analyzed. In some embodiments, theBSRD signal is generated dynamically so as to adaptively maximize (i.e.according to a bandpass or frequency-selection profile) a prominence ofa predetermined non-pulsatile physiological signal within the BSRD. Insome embodiments, the BSRD is subjected to a stochastic orstationary-status analysis. Alternatively or additionally, theneurological or fitness state may be computed from multiple BSRDs,including two or more of: (i) a [sub-200 Hz, ˜300 Hz] BSRD signal; (ii)a [˜300 Hz, ˜1000 Hz] signal; (iii) a [˜1000 Hz, ˜4000 Hz] signal and(iv) a [˜4000 Hz, z Hz] (z>=7,000) signal.

Some embodiments relate to a method for optically measuring state and/orstatus information or changes therein about a warm-blooded subject, themethod including: a. illuminating a portion of the subject's skin ortissue by a vcsel (vertical cavity surface emitting laser) or a diodelaser to scatter partially or entirely coherent light off of thesubject's moving red blood cells (rbcs) to induce a scattered-lighttime-dependent optical response; b. receiving the scattered light by aphotodetector(s) to generate an electrical signal descriptive of theinduced scattered-light time-dependent optical response; c. processingthe scattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom one or moreblood-shear-rate-descriptive (bsrd) signal(s), each bsrd signalcharacterized by a respective frequency-selection profile; d.electronically analyzing features of the bsrd signal(s) of the bsrdsignal group; e. in accordance with the results of the electronicallyanalyzing of the frequency-interval-specific shear-rate-descriptivesignal(s), computing the state and/or status information or changestherein from the results of the analyzing; where: The method alsoincludes a frequency-selection profile of the bsrd(s) signal is computeddynamically so as to adaptively maximize a prominence of a predeterminednon-pulsatile physiological signal within the bsrd(s); and/or. Themethod also includes computation of the state and/or status informationis performed dynamically so that a weight assigned to a bsrd signal isadaptively determined to increase a weight of bsrd signal(s) whosefrequency-selection profile correspond to a greater prominence of thepredetermined non-pulsatile physiological signal at the weight-expenseof bsrd signal(s) whose frequency-selection profile correspond to alesser prominence of the predetermined non-pulsatile physiologicalsignal. Other embodiments of this aspect include corresponding computersystems, apparatus, and computer programs recorded on one or morecomputer storage devices, each configured to perform the actions of themethods.

Implementations may include one or more of the following features. Themethod where the measured state is a neurological state. The method any−4 where the predetermined non-pulsatile physiological signal is a mayerwave signal. The method of any −8 where a prominence of thepredetermined non-pulsatile physiological signal is computed and thestate and/or status information is determined from the results of thecomputing of a prominence of the predetermined non-pulsatilephysiological signal. In some embodiments, where the non-pulsatile bsrdsignal(s) is subjected to a stochastic analysis or to astationary-status analysis that quantifies a stationary/non-stationarystatus of the bsrd signal(s) and the state and/or status information orchanges therein is computed from the results of the stochastic and/orstationary-status analysis. In some embodiments, 20 where: i. apulsatile bsrd signal(s) is also generated from thescattered-light-optical-response-descriptive electrical signal orderived signal thereof, ii. subject-status-classification operation(s)is performed according to both feature(s) of the pulsatile bsrdsignal(s) and the results of the stochastic and/or stationary-statusanalysis of the non-pulsatile bsrd signal(s), iii. the pulsatile bsrdsignal(s) is rated according to a prominence of blood-pressure-waveformfeature(s) therein, and iv. the non-pulsatile bsrd signal(s) isdynamically computed such that the frequency-selection profile thereofis dynamically adjusted. The method any −4 where the predeterminednon-pulsatile physiological signal is a neurogenic signal. The method ofany −4 where the predetermined non-pulsatile physiological signal is amyogenic signal. The method any −4 where the predetermined non-pulsatilephysiological signal is a respiratory signal. The method any −4 wherethe predetermined non-pulsatile physiological signal is aperiodic/oscillator signal. The method where the measured state is afitness state. The method where state and/or status information includesat least one of: a stress-state, a cardiovascular-fitness, a pain-state,a fatigue-state, a stress-resistance, a diurnal fluctuation of stress orstress-resistance, and an apnea event. The method of any −19 where themethod is performed adaptively such that: i. one or more non-pulsatilecandidate bsrd signal(s) are scored so that (a) a greater signal energyand a lower pulsatile signal-contribution increase a quality-score of arated non-pulsatile candidate bsrd signal and (b) conversely, a lowersignal energy and a greater pulsatile signal-contribution decrease aquality-score of a rated non-pulsatile candidate bsrd signal; and ii.the subject-status-classification operation is performed dynamically soas to assign greater weight to candidate bsrd signal(s) having a higherscore and to assign a lower weight to candidate bsrd signal(s) having alower score. The method where the measured state is a neurologicalstate. The method where the measured state is a fitness state. Themethod where state and/or status information includes at least one of: astress-state, a cardiovascular-fitness, a pain-state, a fatigue-state, astress-resistance, a diurnal fluctuation of stress or stress-resistance,and an apnea event. The method where the stochastic and/orstationary-status analysis includes computing at least one of: a fractaldimension of the bsrd signal(s), an entropy of the bsrd signal(s) and ahurst component of the bsrd signal(s). The method where the stochasticand/or stationary-status analysis includes computing at least one of: afractal dimension of the bsrd signal(s), an entropy of the bsrdsignal(s) and a hurst component of the bsrd signal(s). The method wherenon-pulsatile bsrd signal(s) are dynamically computed such that thefrequency-selection profile thereof is dynamically adjusted so as tomaximize a signal energy while minimizing a residual-pulse component ofthe bsrd signal(s). The method where the measured state is aneurological state. The method where the measured state is a fitnessstate. The method where state and/or status information includes atleast one of: a stress-state, a cardiovascular-fitness, a pain-state, afatigue-state, a stress-resistance, a diurnal fluctuation of stress orstress-resistance, and an apnea event. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

One aspect includes a method for optically measuring state and/or statusinformation or changes therein about a warm-blooded subject, the methodincluding: a. illuminating a portion of the subject's skin or tissue bya vcsel (vertical cavity surface emitting laser) or a diode laser toscatter partially or entirely coherent light off of the subject's movingred blood cells (rbcs) to induce a scattered-light time-dependentoptical response; b. receiving the scattered light by a photodetector(s)to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response; c. processing thescattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom one or moreblood-shear-rate-descriptive (bsrd) signal(s), each bsrd signalcharacterized by a respective frequency-selection profile; d.electronically analyzing features of the bsrd signal(s) of the bsrdsignal group to quantify a prominence of a physiological signal withinthe bsrd, the bsrd being selected from the group including of a mayerwave, a neurogenic signal and a myogenic; and e. computing, from theresults of the quantifying of the prominence, the state and/or statusinformation or changes therein. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Themethod where the measured state is a neurological state. The methodwhere the measured state is a fitness state. The method where stateand/or status information includes at least one of: a stress-state, acardiovascular-fitness, a pain-state, a fatigue-state, astress-resistance, a diurnal fluctuation of stress or stress-resistance,and an apnea event. The method where the stochastic and/orstationary-status analysis includes computing at least one of: a fractaldimension of the bsrd signal(s), an entropy of the bsrd signal(s) and ahurst component of the bsrd signal(s). The method where the stochasticand/or stationary-status analysis includes computing at least one of: afractal dimension of the bsrd signal(s), an entropy of the bsrdsignal(s) and a hurst component of the bsrd signal(s). The method wherenon-pulsatile bsrd signal(s) are dynamically computed such that thefrequency-selection profile thereof is dynamically adjusted so as tomaximize a signal energy while minimizing a residual-pulse component ofthe bsrd signal(s). The method where the measured state is aneurological state. The method where the measured state is a fitnessstate. The method where state and/or status information includes atleast one of: a stress-state, a cardiovascular-fitness, a pain-state, afatigue-state, a stress-resistance, a diurnal fluctuation of stress orstress-resistance, and an apnea event. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

One aspect includes a method for optically measuring state and/or statusinformation or changes therein about a warm-blooded subject, the methodincluding: a. illuminating a portion of the subject's skin or tissue bya vcsel (vertical cavity surface emitting laser) or a diode laser toscatter partially or entirely coherent light off of the subject's movingred blood cells (rbcs) to induce a scattered-light time-dependentoptical response; b. receiving the scattered light by a photodetector(s)to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response c. processing thescattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom a non-pulsatileblood-shear-rate-descriptive (bsrd) signal(s), each bsrd signalcharacterized by a respective frequency-selection profile; d. subjectingthe non-pulsatile bsrd signal(s) to a stochastic analysis or to astationary-status analysis that quantifies a stationary/non-stationarystatus of the bsrd signal(s); e. computing the state and/or statusinformation or changes therein from the results of the stochastic and/orstationary-status analysis. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Themethod where the stochastic and/or stationary-status analysis includescomputing at least one of: a fractal dimension of the bsrd signal(s), anentropy of the bsrd signal(s) and a hurst component of the bsrdsignal(s). The method where the stochastic and/or stationary-statusanalysis includes computing at least one of: a fractal dimension of thebsrd signal(s), an entropy of the bsrd signal(s) and a hurst componentof the bsrd signal(s). The method where non-pulsatile bsrd signal(s) aredynamically computed such that the frequency-selection profile thereofis dynamically adjusted so as to maximize a signal energy whileminimizing a residual-pulse component of the bsrd signal(s). The methodwhere the measured state is a neurological state. The method where themeasured state is a fitness state. The method where state and/or statusinformation includes at least one of: a stress-state, acardiovascular-fitness, a pain-state, a fatigue-state, astress-resistance, a diurnal fluctuation of stress or stress-resistance,and an apnea event. Implementations of the described techniques mayinclude hardware, a method or process, or computer software on acomputer-accessible medium.

One aspect includes a method for optically measuring state and/or statusinformation or changes therein about a warm-blooded subject, the methodincluding: a. illuminating a portion of the subject's skin or tissue bya vcsel (vertical cavity surface emitting laser) or a diode laser toscatter partially or entirely coherent light off of the subject's movingred blood cells (rbcs) to induce a scattered-light time-dependentoptical response; b. receiving the scattered light by a photodetector(s)to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response or an ac componentthereof; c. processing the scattered-light-optical-response-descriptiveelectrical signal or a derived-signal thereof to compute therefrom atleast two or at least three or at least fourblood-shear-rate-descriptive (bsrd) signals selected from the bsrdsignal group, each blood-rate-descriptive bsrd signal characterized by adifferent respective frequency-selection profile, the bsrd signal groupincluding of the following signals: (i) a [sub-200 hz, ˜300 hz] bsrdsignal; (ii) a [˜300 hz, ˜1000 hz] bsrd signal; (iii) a [˜1000 hz, ˜4000hz] bsrd signal and (iv) a [˜4000 hz, z hz] (z>=7,000) bsrd signal; d.electronically analyzing features of the at least two or at least 3 orat least 4 bsrd signals of the bsrd signal group; e. in accordance withthe results of the electronically analyzing of the at least two or atleast 3 or at least 4 bsrd signals, computing the state and/or statusinformation or changes therein. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Themethod where the measured state is a neurological state. The methodwhere the measured state is a fitness state. The method where stateand/or status information includes at least one of: a stress-state, acardiovascular-fitness, a pain-state, a fatigue-state, astress-resistance, a diurnal fluctuation of stress or stress-resistance,and an apnea event. Implementations of the described techniques mayinclude hardware, a method or process, or computer software on acomputer-accessible medium.

One aspect includes the method of any −25 where at least one of thenon-pulsatile bsrd signal(s) is subjected to a stochastic analysis or toa stationary-status analysis that quantifies a stationary/non-stationarystatus of the bsrd signal(s) and the state and/or status information orchanges therein is computed from the results of the stochastic and/orstationary-status analysis. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

In some embodiments, the method is performed adaptively such that: i.one or more non-pulsatile candidate bsrd signal(s) are scored so that(a) a greater signal energy and a lower pulsatile signal-contributionincrease a quality-score of a rated non-pulsatile candidate bsrd signaland (b) conversely, a lower signal energy and a greater pulsatilesignal-contribution decrease a quality-score of a rated non-pulsatilecandidate bsrd signal; and ii. the subject-status-classificationoperation is performed dynamically so as to assign greater weight tocandidate bsrd signal(s) having a higher score and to assign a lowerweight to candidate bsrd signal(s) having a lower score. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

Implementations may include one or more of the following features. Insome embodiments, where: i. a pulsatile bsrd signal(s) is also generatedfrom the scattered-light-optical-response-descriptive electrical signalor derived signal thereof, ii. subject-status-classificationoperation(s) is performed according to both feature(s) of the pulsatilebsrd signal(s) and the results of the stochastic and/orstationary-status analysis of the non-pulsatile bsrd signal(s), iii. thepulsatile bsrd signal(s) is rated according to a prominence ofblood-pressure-waveform feature(s) therein, and iv. the non-pulsatilebsrd signal(s) is dynamically computed such that the frequency-selectionprofile thereof is dynamically adjusted. In some embodiments, where themeasuring includes classifying a stress-state so as to distinguishbetween any two of mental-stress, emotional-stress and/or determining ifa dominant stress mode of the subject is physical, emotional or mental.In some embodiments, further including according to thesubject-status-classification operation, (i) triggering at least one ofan alert and therapy and/or (ii) serving advertisement to a user and/or(iii) updating the subject's user-profile and/or (iv) adjustingdisplay-parameter(s) of a gui operated by the user, where at least oneof step(s) c-e is/are performed using a processor. In some embodiments,where the processing of the scattered-light-optical-response-descriptiveelectrical signal or derived signal thereof to compute bsrd signal(s) isperformed by an application-specific integrated circuit (asic) and/or bya circuit (e.g. integrated circuit) in which thefrequency-selection-profile is hardwired into the circuit and/orperformed by a digital signal processor (dsp) (e.g. executing firmware).The method where a plurality of bsrd-specific circuits are employed,each one associated with a different respective frequency-profile. Insome embodiments, where state and/or status information includes atleast one of: a stress-state, a cardiovascular-fitness, a pain-state, afatigue-state, a stress-resistance, a diurnal fluctuation of stress orstress-resistance, and an apnea event. The method where the stress stateis a dominant stress mode of the subject, for example mental vs.emotional. In some embodiments, where the fitness status described anorthostatic physical-stress. In some embodiments, performed onnon-occluded free-flowing blood. The method where the stress statedescribes a magnitude of current-stress of the subject. The method wherethe dynamic weighing of multiple bsrds against each other is performedby execution (e.g. by a—purpose processor and/or microprocessor) ofsoftware such that dynamic-weighing code is present in software. Themethod where the classifying of a stress-state includes. The method mayalso include distinguishing between any two of mental-stress,emotional-stress and/or determining if a dominant stress mode of thesubject is physical, emotional or mental. The method where theclassifying of a stress-state includes quantifying an extent of stressand/or the classifying of the stress-resistance includes classifying astress-resistance-level of the subject. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

One aspect includes a machine-learning-based method for opticallyobtaining state and/or status information or changes therein about awarm-blooded subject, the method including: a. monitoring behaviorpatterns of the subject by camera and/or receiving data via agraphical-user-interface and/or monitoring interactions of the user withadvertisement(s) and/or according to audio output of the user; b.illuminating a portion of the subject's skin or tissue by a vcsel(vertical cavity surface emitting laser) or a diode laser to scatterpartially or entirely coherent light off of the subject's moving redblood cells (rbcs) to induce a scattered-light time-dependent opticalresponse; c. receiving the scattered light by a photodetector(s) togenerate an electrical signal descriptive of the induced scattered-lighttime-dependent optical response d. processing thescattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom one or moreblood-shear-rate-descriptive (bsrd) signal(s), each bsrd signalcharacterized by a respective frequency-selection profile; e. inaccordance with a correlation between (i) a result of the monitoring ofthe subject's behavior patterns of step (a) and (ii) feature(s) of thebsrd signal(s), training a subject-status-classifier capable ofclassifying a subject-status, in accordance with bsrd-signal-derivedinput, at least one a stress-state (e.g. type of stress or level ofstress) a mood-state, a stress-resistance, and a cardiovascularfitness-status of the subject; and f. at a later time, employing thetrained classifier to compute, from the bsrd signal(s), state and/orstatus information or changes therein. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

Implementations may include one or more of the following features. Themethod where the classifying of a stress-state includes. The method mayalso include distinguishing between any two of mental-stress,emotional-stress and/or determining if a dominant stress mode of thesubject is physical, emotional or mental. The method where theclassifying of a stress-state includes quantifying an extent of stressand/or the classifying of the stress-resistance includes classifying astress-resistance-level of the subject. Implementations of the describedtechniques may include hardware, a method or process, or computersoftware on a computer-accessible medium.

One aspect includes apparatus for optically measuring state and/orstatus information or changes therein about a warm-blooded subject theapparatus including: a. a diode laser or vcsel configured to illuminatethe subject's skin so as to scatter partially or entirely coherent lightoff of moving red blood cells (rbcs) of the subject to induce ascattered-light time-dependent optical response; b. photodetector(s)configured to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response; and c. electroniccircuitry configured to: i. process thescattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom one or more blood-shearrate-descriptive (bsrd) signal(s), each bsrd signal characterized by arespective frequency-selection profile; ii. electronically analyzefeatures of the bsrd signal(s) of the bsrd signal group; iii. inaccordance with the results of the electronically analyzing of the atleast two frequency-interval-specific shear-rate-descriptive signals,perform at least one of the following of subject-status-classificationoperation(s). The apparatus also includes classify a stress-state (e.g.type of stress or level of stress) of the subject. The apparatus alsoincludes classify a mood-state of the subject. The apparatus alsoincludes classify a stress-resistance of the subject. The apparatus alsoincludes classify a cardiovascular fitness-status of the subject. Theapparatus also includes where a frequency-selection profile of thebsrd(s) signal is computed. The apparatus also includes dynamically soto adaptively maximize a prominence of a predetermined. The apparatusalso includes non-pulsatile physiological signal within the bsrd(s)and/or where the. The apparatus also includes classification operationis performed dynamically so that a weight. The apparatus also includesassigned to a bsrd signal is adaptively determined to increase a weight.The apparatus also includes of bsrd signal(s) whose frequency-selectionprofile correspond to a. The apparatus also includes greater prominenceof the predetermined non-pulsatile physiological. The apparatus alsoincludes signal at the weight-expense of bsrd signal(s) whosefrequency-selection. The apparatus also includes profile correspond to alesser prominence of the predetermined non. The apparatus also includespulsatile physiological signal. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes apparatus for optically measuring state and/orstatus information or changes therein about a warm-blooded subject theapparatus including: a. a diode laser or vcsel configured to illuminatethe subject's skin so as to scatter partially or entirely coherent lightoff of moving red blood cells (rbcs) of the subject to induce ascattered-light time-dependent optical response, b. photodetector(s)configured to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response, and c. electroniccircuitry configured to perform the following: The apparatus alsoincludes i. processing the scattered-light-optical-response-descriptiveelectrical signal or a derived-signal thereof to compute therefrom oneor more blood-shear-rate-descriptive (bsrd) signal(s), each bsrd signalcharacterized by a respective frequency-selection profile. The apparatusalso includes ii. electronically analyzing features of the bsrdsignal(s) of the bsrd signal group. The apparatus also includes iii. inaccordance with the results of the electronically analyzing of thefrequency-interval-specific shear-rate-descriptive signal(s), computingthe state and/or status information or changes therein from the resultsof the analyzing; where. The apparatus also includes afrequency-selection profile of the bsrd(s) signal is computeddynamically so as to adaptively maximize a prominence of a predeterminednon-pulsatile physiological signal within the bsrd(s); and/or. Theapparatus also includes computation of the state and/or statusinformation is performed dynamically so that a weight assigned to a bsrdsignal is adaptively determined to increase a weight of bsrd signal(s)whose frequency-selection profile correspond to a greater prominence ofthe predetermined non-pulsatile physiological signal at theweight-expense of bsrd signal(s) whose frequency-selection profilecorrespond to a lesser prominence of the predetermined non-pulsatilephysiological signal. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes apparatus for optically measuring state and/orstatus information or changes therein about a warm-blooded subject theapparatus including: a. a diode laser or vcsel configured to illuminatethe subject's skin so as to scatter partially or entirely coherent lightoff of moving red blood cells (rbcs) of the subject to induce ascattered-light time-dependent optical response, b. photodetector(s)configured to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response, and c. electroniccircuitry configured to perform the following. The apparatus alsoincludes i. processing the scattered-light-optical-response-descriptiveelectrical signal or a derived-signal thereof to compute therefrom oneor more blood-shear-rate-descriptive (bsrd) signal(s), each bsrd signalcharacterized by a respective frequency-selection profile. The apparatusalso includes ii. electronically analyzing features of the bsrdsignal(s) of the bsrd signal group to quantify a prominence of aphysiological signal within the bsrd, the bsrd being selected from thegroup including of a mayer wave, a neurogenic signal and a myogenic. Theapparatus also includes iii. computing, from the results of thequantifying of the prominence, the state and/or status information orchanges therein. Other embodiments of this aspect include correspondingcomputer systems, apparatus, and computer programs recorded on one ormore computer storage devices, each configured to perform the actions ofthe methods.

One aspect includes apparatus for optically measuring state and/orstatus information or changes therein about a warm-blooded subject theapparatus including: a. a diode laser or vcsel configured to illuminatethe subject's skin so as to scatter partially or entirely coherent lightoff of moving red blood cells (rbcs) of the subject to induce ascattered-light time-dependent optical response, b. photodetector(s)configured to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response, and c. electroniccircuitry configured to perform the following:. The apparatus alsoincludes i. processing the scattered-light-optical-response-descriptiveelectrical signal or a derived-signal thereof to compute therefrom anon-pulsatile blood-shear-rate-descriptive (bsrd) signal(s), each bsrdsignal characterized by a respective frequency-selection profile. Theapparatus also includes ii. subjecting the non-pulsatile bsrd signal(s)to a stochastic analysis or to a stationary-status analysis thatquantifies a stationary/non-stationary status of the bsrd signal(s). Theapparatus also includes iii. computing the state and/or statusinformation or changes therein from the results of the stochastic and/orstationary-status analysis. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes apparatus for optically measuring state and/orstatus information or changes therein about a warm-blooded subject theapparatus including: a. a diode laser or vcsel configured to illuminatethe subject's skin so as to scatter partially or entirely coherent lightoff of moving red blood cells (rbcs) of the subject to induce ascattered-light time-dependent optical response; b. photodetector(s)configured to generate an electrical signal descriptive of the inducedscattered-light time-dependent optical response; and c. electroniccircuitry configured to perform the following: i. processing thescattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom at least two or at leastthree or at least four blood-shear-rate-descriptive (bsrd) signalsselected from the bsrd signal group, each blood-rate-descriptive bsrdsignal characterized by a different respective frequency-selectionprofile, the bsrd signal group including of the following signals: (i) a[sub-200 hz, ˜300 hz] bsrd signal; (ii) a [˜300 hz, ˜1000 hz] bsrdsignal; (iii) a [˜1000 hz, ˜4000 hz] bsrd signal and (iv) a [˜4000 hz, zhz] (z>=7,000) bsrd signal; ii. electronically analyzing features of theat least two or at least 3 or at least 4 bsrd signals of the bsrd signalgroup; iii. in accordance with the results of the electronicallyanalyzing of the at least two or at least 3 or at least 4 bsrd signals,computing the state and/or status information or changes therein. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

One aspect includes where the measured state is a neurological state.Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

One aspect includes where the measured state is a fitness state. Otherembodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.

One aspect includes where state and/or status information includes atleast one of: a stress-state, a cardiovascular-fitness, a pain-state, afatigue-state, a stress-resistance, a diurnal fluctuation of stress orstress-resistance, and an apnea event. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes where the predetermined non-pulsatile physiologicalsignal is a mayer wave signal. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes where the predetermined non-pulsatile physiologicalsignal is a neurogenic signal. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes where the predetermined non-pulsatile physiologicalsignal is a myogenic signal. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes where the predetermined non-pulsatile physiologicalsignal is a respiratory signal. Other embodiments of this aspect includecorresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

One aspect includes where the predetermined non-pulsatile physiologicalsignal is a periodic/oscillator signal. Other embodiments of this aspectinclude corresponding computer systems, apparatus, and computer programsrecorded on one or more computer storage devices, each configured toperform the actions of the methods.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1B schematically illustrate blood flow within a blood vessel.

FIG. 2A shows information about physiological oscillators.

FIG. 2B shows a pulsatile wave-form.

FIGS. 3A-3B and 7A-7B shows a prior art system (or portions thereof) anda prior art method for in-vivo dynamic light scattering.

FIG. 4 describes an optical response signal.

FIGS. 5A-5B describe a pulsatile and non-pulsatile BSRD.

FIGS. 6A-6B describe data-flow related to DLS.

FIGS. 8, 9A-9B, 22B, 25, show data flow according to some embodiments.

FIGS. 10, 12A-12B, 16-17, 18A-18C, 22A, 23-24 are flow charts ofpresently disclosed methods according to some embodiments.

FIGS. 14A-14B show time windows.

FIGS. 11A-11B, 13, 19, 20A-20B, 21A-21B, 26A-26H, 27A-27D, 28A-28D,29A-29D, 30A-30D show data and/or results according to some embodiments.

FIGS. 31A-31C describe power spectrum amplitudes.

FIGS. 32A-32B, 33A-33B, 34A-34B, 35A-35B, and 36A-36B illustrateadditional embodiments.

DETAILED DESCRIPTION OF EMBODIMENTS

The invention is herein described, by way of example only, withreference to the accompanying drawings. With specific reference now tothe drawings in detail, it is stressed that the particulars shown are byway of example and for purposes of illustrative discussion of thepreferred embodiments of the exemplary system only and are presented inthe cause of providing what is believed to be a useful and readilyunderstood description of the principles and conceptual aspects of theinvention. In this regard, no attempt is made to show structural detailsof the invention in more detail than is necessary for a fundamentalunderstanding of the invention, the description taken with the drawingsmaking apparent to those skilled in the art how several forms of theinvention may be embodied in practice and how to make and use theembodiments.

For brevity, some explicit combinations of various features are notexplicitly illustrated in the figures and/or described. It is nowdisclosed that any combination of the method or device featuresdisclosed herein can be combined in any manner—including any combinationof features—and any combination of features can be included in anyembodiment and/or omitted from any embodiments.

Embodiments of the present invention relate to apparatus and method foroptically detecting stress and/or mood and/or emotion and/or fitnessand/or stress-resistance of a warm-blooded (e.g. mammalian or bird—insome preferred embodiments, the warm-blooded subject is a human subject)subject based on dynamic light scattering of red blood cells (RBCs)moving in the vessels.

In the prior art, dynamic light scattering has been used to generate apulsatile Blood Shear Rate Descriptive signal (BSRD) and to computetherefrom pulse rate and blood pressure. The pulse signal is a knownindicator of emotion, stress and fitness.

Yet, specifically by relating to filtering out the pulsatile signal as‘noise’ (and/or by employing an appropriate non-pulsatilefilter-selection profile for BSRD(s) generation to generate anon-pulsatile BSRD) is it possible to improve the accuracy and/or reducethe noise when detecting of emotion, stress and/or fitness/

A non-pulsatile BSRD may be generated from theresponse-descriptive-electrical signal (or a derivative thereof) usingthe appropriate frequency-selection profile—e.g. for example, [a Hz, bHz] where b>a e.g. b at most 1000 or at most 950 or at most 900 or atmost 800 or at most 700 or at most 600 or at most 500 or at most 400 orat most 350 or at most 300). Alternatively or additionally, pulsatilecomponent(s) of a pulsatile BSRD may be substantially removed (e.g.using a band-pass filter that filters out frequencies having significantpulsatile components) therefrom to generate the non-pulsatile BSRD whichmay be analyzed in the absence of the ‘distracting’ ‘noise’ pulsatilecomponents. Thus, even though it is recognized that the pulse signalform (FIG. 2B) may include data about stress or other related indication(e.g. when someone is nervous his/her pulse increases), in someembodiments instead of relating to the pulsatile components of the BSRDas ‘signal’ it is more appropriate to strip them off as ‘noise.’

In different embodiments, one or more (i.e. any combination) of thefollowing feature(s) is provided:

A. Dynamic operation-mode—a BSRD (e.g. non-pulsatile BSRD) isdynamically generated using a ‘dynamic frequency selection profile’updated in response to a prominence of feature(s) of certainphysiological signals—for example, to dynamically-updated maximize apredicted or prominence of the physiological signal(s) within theBSRD—in some embodiments, see, for example, FIGS. 12-14, 17, 23-25);

B. Stochastic analysis of non-pulsatile BSRD(s)—non-pulsatile BSRD(s)(e.g. non-pulsatile BSRD) is generated and analyzed (e.g. subjected tostochastic analysis), and the detection of stress and/or mood and/oremotion and/or fitness is performed in accordance with the results ofthe analysis (see FIG. 18A-18C);

C. Weighing (e.g. dynamic weighing) of multiple types of BSRD(s)multiple types of BSRD(s) are generated (e.g. comprising at least onenon-pulsatile BSRD,) each one associated with a respective type offrequency selection profile. In some embodiments, when detecting stressand/or mood and/or emotion and/or fitness or a warm-blooded (e.g.mammalian) subject to ‘classify’ the subject's status, a dynamicweighing may the assigned to each type of BSRD—the relative weights maydepend on (and be updated in response to changes in) the specifics ofthe mammalian subject or measurement conditions or on any other factor.Alternatively or additionally, one or more frequency selectionprofile(s) for generating a BSRD may be dynamically selected. (see FIGS.22-25);

D. a machine-learning technique for better sensor accuracy—In someembodiments, it is possible to ‘eavesdrop’ on the subject's behavior byboth (i) DLS dynamic light scattering techniques where a BSRD isgenerated upon scattering light from the subject's red blood cells atspecific times and (ii) non-DLS data descriptive of the subject'sinstant stress-state or mood state at these specific times. ABSRD-feature-based mood-state-classifier or stress-state-classifier maybe trained or updated according to the relation between the DLS data andthe stress-state or mood-state descriptive non-DLS data. For example,BSRD-feature-based mood-state-classifier or stress-state-classifier maybe trained so as to determine optimal frequency-selection profiles (i.e.for BSRD generation) and/or to determine optimal weights betweenmultiple BSRD signals that optimize prediction stress-state ormood-state prediction accuracy

One example of non-DLS data explicitly (or implicitly) descriptive ofuser mood or stress-state include is GUI-input data generated by humaninteraction with a graphical user interface (GUI)—for example, via atouch-screen or keyboard or mouse by an ‘observing camera.’ In a firstuse-case, a user is using a personalized music-listening application(e.g. local or cloud-based) where a user selects song from a ‘bank’ ofsongs to which to listen. In this use-case, it may be possible to‘eavesdrop’ on the user's music-selections—if the user selects a ‘sad’song this might be indicative that the user is feeling ‘sad’—at thistime, it may be possible to use this as calibration data for futureDLS-based mood detection by generating BSRD signal(s) and computingfeatures thereof.

In a second use-case, it is possible to eavesdrop on a user's voice(e.g. spoken speech) or typed output (i.e. text input to a digitalcomputer—e.g. via a keyboard) and to derive therefrom mood-status orstress-status data about the subject. This may be derived according tothe language content—for example, the user types ‘I am happy.’ Inanother example, a subject's instant stress or mood-status may becomputed according to biometric data (e.g. voice-print data or biometrictyping patterns).

Other non-DLS techniques for gathering data about the subject's instantmood-state or stress-state include but are not limited to: (i) capturing(e.g. by camera) or receiving digital images of the subject's facialexpressions—it is possible using image-processing techniques to computethe subject's stress-state or mood-state from an image of his/her face;(ii) eavesdropping on user interaction with a GUI—for example, if songsor advertisements are sent to a user and the user ‘skips’ certain songs(or elects to listen to them) this indicates the user's instantmood-state or stress-state.

Non-DLS data about the user's mood-state or stress-state may be employedto train a DLS-based classifier so that a later time (e.g. when thenon-DLS data is not available), the trained DLS classifier may beemployed to accurately sense the subject's mood-state or stress-state.

During training of a DLS/BSRD-based mood-state or stress-stateclassifier, one or more of the following parameter(s) (the list below isnot intended as comprehensive) of the stress/mood classifier may beoptimized so as to maximize a prediction power of the DLS/BSRD-basedmood-state or stress-state classifier: (i) a frequency profile for BSRDgeneration; (ii) a weighing function for relative weight BSRD(s) whereeach BSRD has its own respective frequency-selection parameter.

E. Response (e.g. treatment to reduce stress and/or improve mood)—inresponse to a detection of an elevated stress or to a ‘poor mood’ anumber of measures may be taken including but not limited to: (i)subjecting the mammalian subject to ‘relaxing’ images or lighting orsound (e.g. music or sound)—e.g. on a display screen or via aspeaker—for example, selecting from a ‘pleasant’ song from a database or‘bank’ of ‘candidate songs.’ Alternatively or additionally, a ‘flash oflight’ or ‘light therapy’ may be provided—e.g. by light box; (ii)controlling the temperature in a location (e.g. in the room) where asubject is located—e.g. in the winter (summer) if a subject is‘stressed’ the stress might be treated by increasing (decreasing) thetemperature in the room. Thus, in some embodiments, in response to aDLS/BSRD-based determining of the stress-state or mood-state of thesubject, a signal is sent to a device for regulating an indoortemperature (e.g. heating device or an air-conditioning device and/or anHVAC (heating, ventilating, and air conditioning; also heating,ventilation, and air conditioning) system—for example, to increase ordecrease a set-point temperature.

Another example relates to modifying operating parameter(s) of a userinterface For example, in response to a DLS/BSRD-based determinationthat a subject is in a ‘high-stress state’ or in a ‘bad mood’ (e.g. sador depressed), an operating parameter of a GUI may be modified—e.g. toincrease a font-size (making text easier to read) or to modifybackground color to a more ‘relaxing’ color (e.g. more of a blue shade)or any other operating parameter.

In yet another example, advertising may be served to a user in responseto a DLS/BSRD-based determination of mood and/or stress—e.g. in responseto a determining that the user is in a ‘good mood’ or in a ‘low stressstate’ an advertisement for a more expensive item (e.g. luxury item) maybe served. Alternatively or additionally, in response to a determiningthat the user is in a ‘bad mood’ or “high stress state’ an advertisementfor ‘comfort food’ may be served to the user.

In yet another example, advertising may be served to a user in responseto a DLS/BSRD-based determination of fitness parameter—e.g. if the useris deemed to be ‘not fit’ an advertisement for a product to remedy thesituation (e.g. exercise equipment) may be served to the user.

In yet another example related to ‘treating stress and/or mood’ (i) anelectrical signal may be sent (e.g. via an electrode) to stimulate thesubject and/or (ii) an electric (or electromagnetic) protocol fortreating depression may be updated.

In yet another example, a monitored subject is using an electroniccommunication network (e.g. a packet-switched network or the Internet ora cellular network) and in response to a DLS/BSRD-based determination ofmood and/or stress, the amount of bandwidth allocated to the user may bemodified—e.g. if the user is ‘stressed’ or ‘in a bad mood’ the amount ofbandwidth may be increased.

In yet another example, an alert-signal or alarm-signal may begenerated—e.g. in response to a determining of an apnea-incident or a‘bad’ mood or stress-state.

Discussion of FIGS. 9A-9B

FIG. 9A is a block diagram of a system for detecting stress and/or moodand/or emotion and/or fitness and/or stress-resistance of a mammaliansubject. Not every element in FIG. 9 is required, and no attempt hasbeen made to illustrate every element provided in all embodiments of theinvention.

The system of FIG. 9A comprises: a source 100 of at least partiallycoherent light, photodetector 110, optional analog subtraction circuitry120, BSRD(s) generating signal processor(s) 130, BSRD signal analyzer(s)140 and stress/mood/emotion/fitness classifier 150. Any other theseelements may be implemented in, or include ‘electronic circuitry.’

FIG. 9B is like FIG. 9A—however, in FIG. 9B, element 130 generates BSRDsignal(s) according to

In the present disclosure ‘electronic circuitry’ is intended broadly todescribe any combination of hardware, software and/or firmware.

In one particular example, power spectrum integral (or generating BSRDby any other method) may be performed by ASIC or customized hardwareand/or DSP (e.g. configured by firmware). Thus, in some embodiments,this may be for generating the BSRD or for analyzing the BSRD (e.g.computing an integral (e.g. power spectrum integral) thereof.

Electronic circuitry may include any executable code module (i.e. storedon a computer-readable medium) and/or firmware and/or hardwareelement(s) including but not limited to field programmable logic array(FPLA) element(s), hard-wired logic element(s), field programmable gatearray (FPGA) element(s), and application-specific integrated circuit(ASIC) element(s). Any instruction set architecture may be usedincluding but not limited to reduced instruction set computer (RISC)architecture and/or complex instruction set computer (CISC)architecture. Electronic circuitry may be located in a single locationor distributed among a plurality of locations where various circuitryelements may be in wired or wireless electronic communication with eachother.

In some embodiments, element 120 is implemented as shown in FIG. 7B.

Photodetector(s) 110 generate an electrical signal descriptive of theinduced scattered-light time-dependent optical response. After initialprocessing of this “electrical signal descriptive of the inducedscattered-light time-dependent optical response” the result may still bean electrical signal descriptive of the induced scattered-lighttime-dependent optical response—for example, initial processing may beperformed by analog circuitry of FIG. 7B. Examples of ‘generating’ anelectrical signal descriptive of the induced scattered-lighttime-dependent optical response are presented in flow charts below—see,for example, step S355 of FIG. 10, step S205 of FIG. 12, step S405 ofFIGS. 18A-18B,

In some embodiments, the BSRD signal processor 130 is configured todynamically generate the BSRD (see, for example, step S359 of FIG. 10,step S309-323 of FIG. 12, steps S601, S619 and S623 of FIG. 16).Alternatively or additionally, BSRD signal processor 130 may beconfigured to generate non-pulsatile BSRD(s).

The BSRD(s) generated by BSRD-generating signal-processor 130 areanalyzed by BSRD signal analyzer(s) 140 (see, for example, step S363 ofFIG. 10, steps S609-S13 of FIG. 16,

As will be discussed below, in some embodiments, BSRD-generatingsignal-processor 130 receives feedback derived from the results of BSRDsignal analyzer(s).

Element 150 is to compute and/or predict (and/or classify a state of thewarm-blooded subject (e.g. mammalian subject—for example, human) astress state and/or dominant type of stress and/or emotion state and/orfitness parameter of the subject—see, for example, step S369 of FIG. 10,step S327 of FIG. 12.

A Comment about Response—in any embodiment, for any analysis ordetermining or computing of a state (or resistance or any otherparameter) of a warm-blooded subject (e.g. stress-state, mood-state,emotion-state, apnea-state, stress-resistance, cardiovascular fitnessstate) disclosed herein, one or more (i. any combination) of responsesmay be optionally provided. These response may include: (i) presentingto a user (e.g. visually on a display-screen or by audio means—forexample using a speaker) a description of the determined state; (ii)trigger an alarm or alert signal (iii triggering therapy (e.g. massage,food, image/drug, resistance for an exercise machine, temperature, serveaudio (e.g. music) or video (e.g. video), or smell, light frequencymodulation (e.g. hypnosis), biofeedback) to reduce a stress state orimprove a mood or fitness; (iii) serving advertisement to a user—e.g. wetime the advertisements for when the user is in a good mood he will bemore likely to respond positively—the proper moment. E.g. If the user isin a bad mood, an advertisement certain ‘mood-improving items’ (e.g.sweet foods or relaxing beverages) may be served; (iv) updating thesubject's user-profile (v) adjusting display-parameter(s) of a GUIoperated by the user (vi) upgrading or downgrading use-privileges—forexample, if a user is stressed his/her available bandwidth might bereduced to reduce his/her stress level (thereby increasing useprivileges)—in another example, when a user of a motorized vehicle isstressed the maximum speed that s/he is permitted to drive may bereduced for safety reasons, thereby downgrading use privileges; (vii)subjecting the user to additional test of stress level or mood (e.g. byvoice-print or processing an image of face or in any other manner) (vii)matching (e.g. dating, business matching, etc)—social networking (ix)social networking—to suggest additional friends; (x) presenting a listof search results that is biased by the user's mood or stress-state orstress-resistance; (xii) serving a user food or beverage adapted to theuser's mood or stress-state or stress-resistance—for example, afood/beverage dispenser may increase caffeine or alcohol or sugarcontent to a ‘unhappy user’

In some embodiments, any technique disclosed herein may be used tomeasure and/or respond to fatigue or substance-addiction or pain.

Overview of FIGS. 10, 12, 16, 18A-18C, 22A, 23

FIGS. 10, 12, 16, 18A-18C, 22A, 23 are steps of methods for detecting aemotional state, a stress-state, mood-state, and/or cardiovascularfitness parameter of warm-blooded (e.g. mammals or birds) individuals.

Brief Description of FIGS. 10-11 (computing mood and/or stress and/orfitness and/or emotion according to strength of a contribution of asub-Hz frequency-band(s) and/or signal to a BSRD)—one salient feature ofthe method of FIG. 10 is that sub-Hz frequency(ies) of a BSRD areanalyzed (e.g. frequencies of neurogenic and/or myogenic and/orrespiratory oscillators of the blood-shear signal—e.g. non-pulsatilecomponent(s) of the shear signal)—for example, specific featuresthereof. These specific feature may include relative energycontributions at specific bandpass frequencies to an overall BSRD signaland/or a ratio between energy contributions in different sub-Hz ‘bands’(below the pulsatile) and/or prominence of a sub-Hz physiological signal(e.g. neurogenic and/or myogenic and/or respiratory signal presentwithin the blood-shear signal—Mayer wave) within a BSRD. Thus, in onenon-limiting example related to a specific sub-Hz physiological signal(Mayer wave), it is possible to compute a strength of a ‘Mayer wave’contribution to a BSRD signal (e.g. generated by scattering coherentlight from RBCs. In response to detecting a relatively ‘strong’ Mayerwave contribution within a BSRD, an indication is provided that thewarm-blooded subject's stress-state is ‘high mental stress.” This may bemeasured and corroborated by a Stroop test (see FIG. 11A and thediscussion below).

Thus, FIG. 10 is a flow chart of a method for sensing emotion and/ormood and/or a stress-state and/or a cardiovascular fitness parameter ofa subject. Steps S351 and S355 respectively correspond to steps S201 andS205 and FIG. 3B. In step S359 a time-dependent BSRD signal is generatedfrom the scattered optical response descriptive electrical signal or aderive signal thereof—e.g. using any algorithm or combination ofalgorithms disclosed in WO 2008/053474 or WO2012064326 and/or US20150141766, each of which is incorporated herein by reference in itsentirety. Thus, in some embodiments, BSRD(s) generatingSignal-processor(s) 130 implements any algorithm or any algorithm orcombination of algorithms disclosed in WO 2008/053474 or WO2012064326and/or US 20150141766.

These algorithms for generating a BSRD are really a family of algorithmsthat are parameterized by a frequency selection profile. Thus, indifferent embodiments, frequency selection profiles other than thosedisclosed in WO 2008/053474 or WO2012064326 and/or US 20150141766 areemployed.

Examples of frequency selection profiles disclosed in WO 2008/053474 arethe frequency ‘windows’ PwS of the power spectrum—for example, [0 Hz,550 Hz,] and [2700 Hz, 10000 Hz]. These windows are essentiallystep-functions or band-pass filters.

Definitions

For convenience, in the context of the description herein, various termsare presented here. To the extent that definitions are provided,explicitly or implicitly, here or elsewhere in this application, suchdefinitions are understood to be consistent with the usage of thedefined terms by those of skill in the pertinent art(s). Furthermore,such definitions are to be construed in the broadest possible senseconsistent with such usage.

The term ‘stress’ may refer to detecting a stress-state or detecting arelaxation state—i.e. the relative presence or absence of stress. Forthe present disclosure, stress refers to non-physical stress—inparticular, to emotional stress or mental stress. Non-physical stressmay be (i) emotional stress (e.g. in response to an (un)pleasant soundor temperature or smell or ‘good news’ or ‘bad news’ or in response toany other negative or positive stimulus; in another example, an attemptto lie may trigger emotional stress; in another example, good news mayrelieve tension and lead to an absence of stress—especially, right afterhearing the good news) or (ii) mental stress (e.g. attempting to solve apuzzle or to perform mathematics; in another example, when a student iscompleting his/her homework this may be mental stress; when attemptingto draft a patent application, this is also mental stress). The term‘emotion’ and ‘mood’ are used interchangeably.

Computing a ‘state’ of a subject may include: (i) quantifying amagnitude of a state of the subject—to distinguish between aslightly-happy and extremely happy subjects or to determine if a subjectis slightly stressed or extremely stressed; (ii) computing a stress-load(e.g. mental-load in the case of mental stress)—i.e. a magnitude ofstress—i.e. to quantify a presence or absence of stress; (iii)differentiating between two or more candidate states (e.g. to determineif a subject is more ‘happy’ than ‘anxious,’ or more ‘happy thanangry’); (iv) determining a dominant state among two or morecandidate-states (e.g. to determine if a dominant stress state ismental-stress or emotional-stress).

The term “physiological response signal” refers to a physiologicalresponse (i.e. as manifested in blood flow) to input and/or feedbackfrom the central nervous system. Examples of physiological responsiblesignals include (with reference to FIG. 2A) metabolic response signals,neurogenic response signals, myogenic response signals, respiratoryresponse signals and heart/pulse signals response. The signal can bedescribed in the time domain or the frequency domain (e.g. as a powerspectrum).

Some embodiments relate to a ‘non-pulsatile’ signal (BSRD)—stronglypulsatile vs. weakly pulsatile vs non-pulsatile signals may bedetermined and defined according to the power spectrum of the signal(e.g. BSRD). A signal (e.g. BSRD) may be pulsatile (see FIG. 31A) ifthere are clear power peaks in the range between 0.4 Hz and 3.5 Hz—thusin FIG. 31A the ‘strong peak’ is the pulse and this is clearly apulsatile BSRD. In FIG. 31B, the peak is still present but not asstrong—this is for a ‘weakly puslatile’ (but still pulsatile BSRD). InFIG. 31C, there are no clear peaks for the power spectrum of the BSRD inthe range between 0.4 Hz and 2 Hz. The intention of this definition of‘non-pulsatile’ signals is not intended the definition known in the artby the skilled artisan, but merely to harmonize with/clarify thedefinition known in the art by the skilled artisan.

A ‘stress-resistance’ relates to stress-states as follows: (i) a subjectmay be subjected to a stressful stimulus (where the stress stimulus maybe quantified to distinguish between a ‘small stimulus’ and a ‘largestimulus’); (ii) the subject's stress-state before the stimulus andafter the stimulus may be quantified. In the event that a ‘small’ or‘minor’ stress stimulus induces a relatively large ‘increase instress-state’ this may be indicative of a low stress-resistance.Conversely, in the event that a ‘large or ‘major’ stress stimulus onlyinduces a relatively small ‘increase in stress-state’ this may beindicative of a high stress-resistance.

Electronic circuitry may include may include any executable code module(i.e. stored on a computer-readable medium) and/or firmware and/orhardware element(s) including but not limited to field programmablelogic array (FPLA) element(s), hard-wired logic element(s), fieldprogrammable gate array (FPGA) element(s), and application-specificintegrated circuit (ASIC) element(s). Any instruction set architecturemay be used including but not limited to reduced instruction setcomputer (RISC) architecture and/or complex instruction set computer(CISC) architecture. Electronic circuitry may be located in a singlelocation or distributed among a plurality of locations where variouscircuitry elements may be in wired or wireless electronic communicationwith each other.

“Computer storage’ (or just ‘storage’) is volatile (e.g. RAM) and/ornon-volatile (e.g. magnetic medium or flash) memory readable by anelectronic device (e.g. digital computer).

Analog electrical signals or light fields may comprises more than onesub-signal added together in a single electrical (or optical) signal.For example, an analog electrical signal derived from a light fielddetected by a photodetector that (i.e. where scattered light that isscattered from particles within a fluid contributed to the light field)may be the sum of: (i) a first component (i.e. analog electricalsub-signal) attributable to ambient light (e.g. sunlight); (ii) a secondcomponent attributable to skin light-modulating effects; (iii) a thirdcomponent attributable to regular fluctuations in light intensity due tothe presence of a fluorescent bulb and (iv) a fourth componentattributable to scattered light that is scattered from particles withina fluid contributed to the light field. Each component or sub-signal ofthe analog electrical signal is associated with a different respectiveamount of power.

In some examples, for an analog signal generated by a photodetector, therelative power contribution to overall analog signal power attributableto ambient light is relatively high (i.e. the first component), whilethe relative power contribution to overall analog signal powerattributable to scattered light that is scattered from particles withina fluid is relatively low (i.e. second component).

In general, both a signal and a sub-signal have power levels—thefraction of the power level of the overall signal attributable to aparticular portion of the signal or sub-signal is the ‘power fraction’of the sub-signal or signal component. In the example of the previousparagraph, the power fraction of the overall analog electrical signaldue to the ambient light component may be significant (e.g at least 0.1or at least 0.3 or at least 0.5) while the power fraction of the overallanalog electrical signal due to the ‘light scattering’ component (i.e.fourth component) may be relatively low—for example, at most 0.1 or atmost 0.05 or at most 0.01).

Embodiments of the present invention relate to generating a ‘hybrid’signal. A ‘hybrid signal’ derived from a plurality of input analogsignals is any non-zero or non-trivial mathematical combination of theinput analog signals—i.e. including multiplication, addition,subtraction, etc. The term ‘hybrid’ refers to the fact that the output(or hybrid) signal relates to more than one input signal, and is notrestricted to a single input.

Embodiments of the present invention relate to photodetectors (anytechnology may be used including those listed herein or any othertechnology). In some embodiments, each photodetector is notinfinitesimally small but rather has a size. The ‘distance’ betweenphotodetectors relates to a centroid-centroid distance.

In some embodiments, a light field is comprised of more than oncomponent. Whenever light is generated and reflected or scattered (ormodulated in any other manner) to introduce photons into (or to passthrough) a certain location (and/or to illuminate the location), thislight ‘contributes to’ or ‘influences’ the local light field at thatcertain location.

Embodiments of the present invention relate to optically measuring aparameter relating to a subject. In different embodiments, this subjectis human, or a mammal other than human, or to a warm-blooded animalother than mammals (e.g. birds).

Whenever a power level of a second signal is ‘significantly less’ than apower level of a first signal, a ratio between a power level of thesecond signal and a power level of the first signal is at most 0.5 or atmost 0.3 or at most 0.2 or at most 0.1 or at most 0.05 or at most 0.01.

Some embodiments of the present invention are described for the specificcase of only two photodetectors and/or measuring a light field in twolocations. The skilled artisan will appreciate that this is not alimitation, any teaching disclosed herein may relate to the case of morethan two photodetectors or detecting light fields in more than twolocations. Thus, two photodetectors refers to ‘at least two,’ twolocations' refers to at least two, and so on.

A product of a ‘first signal’ is a second signal that is derived fromthe first signal—this does not require ‘multiplication.’

A ‘derivative’ of a ‘signal’ is a signal that is derived therefrom—thisdoes not require computing a ‘mathematical derivative’ as is known incalculus.

‘Quantifying a correlation’ between two functions or data-sets refers tocomputing a slope between the data sets of some of the parameter ofcurvefitting (linear or non-linear) or a goodness of a fit.

For any apparatus disclosed herein, a “source of partially or entirelycoherent light” may be, but is not required to be, a vertical-cavitysurface-emitting laser VSCEL.

The [a Hz, b Hz] notation (both a and b are non-negative real numbers,b>a) used in WO 2008/053474 to describe ‘frequency windows’ is used todescribe a ‘frequency selection profile. The same [a Hz, b Hz] notationis used to describe a ‘frequency selection profile’ and a BSRD. A [a Hz,b Hz]

For the present invention, when an input signal (e.g. a BSRD signal orscattered-light time-dependent optical response signal) is subjected toa frequency selection profile, some frequencies of the input signal areretained and other frequencies selectively are rejected. One example ofa ‘frequency selection profile’ is a ‘frequency window’/stepfunction/band-pass filter—however, this is not a limitation—otherfilters include but are not limited to Butterworth filters, Chebyshevfilters, and Elliptic filters. In the case of a ‘band-pass filter,’ 100%of energy of the input signal is rejected at frequencies outside of the‘window’—however, this is not a limitation and in other examples, mostbut not all energy of the input signal may be rejected outside of‘frequency range’ defining the frequency selection profile.

As noted above, the same [a Hz, b Hz] notation is used in WO 2008/053474is used to describe a ‘frequency selection profile’—however, they do notmean the same exact thing. A [a Hz, b Hz] frequency selection profileretains at least 65% of (in some embodiments, at least 75% or at least90% or at least 95%) of energy of the input signal for frequencies of atleast a Hz and at most b Hz, and rejects at least 65% of (in someembodiments, at least 75% or at least 90% or at least 95%) of energy forfrequencies of less than a Hz and for frequencies greater than b Hz.

The [a Hz, b Hz] notation (both a and b are non-negative real numbers,b>a) used in WO 2008/053474 in the context of defining a frequencywindow is not to be confused with the notation [a Hz, b Hz] BSRD. Forthe present disclosure, a [a Hz, b Hz] BSRD signal (both a and b arenon-negative real numbers, b>a) is a BSRD signal where at least 50% orat least 75% or at least 90% or at least 95% or at least 99% of theenergy of the BSRD signal has a frequency of at least a Hz and at most bHz. A x % [a Hz, b Hz] BSRD signal is a specific type of [a Hz, b Hz]BSRD signal such that at least x % of the energy of the signal has afrequency of at least a Hz and at most b Hz. By definition, every [a Hz,b Hz] BSRD signal is at 50% [a Hz, b Hz] BSRD signal. For the presentdisclosure, any [a Hz, b Hz] BSRD signal disclosed herein may be a 50%[a Hz, b Hz] BSRD signal or a 75% [a Hz, b Hz] BSRD signal or a 90% [aHz, b Hz] BSRD signal or a 95% [a Hz, b Hz] BSRD signal or a 99% [a Hz,b Hz] BSRD signal.

Some embodiments relate to a [(a₁, a₂) Hz, (b₁, b₂) Hz] BSRD signalwhere (i) (a₁, a₂) refers to the range of numbers between a₁ and a₂ (ii)b₁, b₂ refers to the range of numbers between b₁ and b₂ and (ii) a₂>a₁and b₂>b₁. For the present disclosure, a [(a₁, a₂) Hz, (b₁, b₂) Hz] BSRDsignal is a [a₁ Hz, b₂ Hz] BSRD signal. A [(a, Hz, (b₁, b₂) Hz] BSRDsignal (where a<b1<b2) is a [a Hz, b₂ Hz] BSRD signal. A [(a₁, a₂) Hz, bHz] BSRD signal (where a1<a2<b) is a [a₁ Hz, b Hz] BSRD signal

For the present disclosure, sub-Hz frequencies are frequencies of atmost 1 Hz. Sub 0.5-Hz frequencies are frequencies of at most 0.5 Hz. Sub0.25-Hz frequencies are frequencies of at most 0.25 Hz. In anyembodiment, ‘sub-Hz’ frequencies may refer to sub 0.5-Hz frequencies orsub-0.25 Hz frequencies.

A sub-Hz frequency selection profile, when applied to an input signal(e.g. a BSRD signal or scattered-light time-dependent optical responsesignal) rejects at least a majority (in some embodiments, at least 75%or at least 90% or at least 95% or at least 99%) of energy the inputsignal for most frequencies less than 1 Hz, and retains at least amajority (in some embodiments, at least 75% or at least 90% or at least95% or at least 99%) of energy for most frequencies greater than 1 Hz.The same definition applies for sub-0.25 Hz frequency selection profilewhere ‘0.25 Hz’ is substituted for 1 Hz. (in some embodiments, at least75% or at least 90% or at least 95% or at least 99%).

A ˜300 Hz frequency has a value of (i) at most 500 Hz or at most 450 Hzor at most 400 Hz or at most 350 Hz and (ii) at least 200 Hz or least250 Hz.

A ˜1000 Hz frequency has a value of (i) at most 1500 Hz or at most 1250Hz or at most 1200 Hz or at most 1100 Hz and (ii) at least 750 Hz or atleast 850 Hz or at least 900 Hz.

A ˜4000 Hz frequency has a value of (i) at most 2500 Hz or at least 3000Hz or at least 3500 Hz and (ii) at most 7500 Hz or at most 6000 Hz or atmost 5000 Hz.

‘Sub Hz’ frequencies are frequencies less than 1 Hz. ‘Sub 0.5 Hz’frequencies are frequencies less than 0.5 Hz. ‘Sub 0.25 Hz’ frequenciesare frequencies less than 0.25 Hz.

Reference is made once again to FIG. 10. In step S363, the BSRD signal(for example, a ‘non-pulsatile BSRD signal’) is analyzed to compute oneor more of: (i) (A) A (magnitude of) a relative energy contribution ofsub-Hz frequencies (e.g. sub 0.5-Hz frequencies or sub 0.25 Hzfrequencies) to the BSRD signal; (B) a ratio between relative energycontributions of (i) a first sub-Hz portion of the BSRD signal extractedtherefrom according to a first sub-Hz frequency selection profile (e.g.according to a first sub-0.5 Hz frequency selection profile, oraccording to a first sub-0.25 Hz frequency selection profile); and (ii)a second sub-Hz portion of the BSRD signal extracted therefrom accordingto a second sub-Hz frequency selection profile (e.g. according to asecond sub-0.5 Hz frequency selection profile, or according to a secondsub-0.25 Hz frequency selection profile); (C) Prominence of features ofa sub-Hz physiological signal (e.g. a sub 0.5-Hz physiological signal ora sub-0.25 Hz physiological signal) within the BSRD signal—for, examplenon-pulsatile physiological signal.

In step S369, the mood-state and/or emotion-state and/or stress-state(e.g. instant or immediate state) of the subject is computed.Alternatively or additionally, a cardiovascular fitness parameter iscomputed.

With reference to FIG. 2A, it is noted that respiratory oscillations ofthe blood shear signal is an example of a sub-0.5 Hz physiologicalsignal. Examples of sub-0.25 physiological signals are (i) myogenicoscillations of the blood shear signal; (ii) neurogenic oscillations ofthe blood shear signal.

FIG. 11A illustrates one example of the results of performing the methodof FIG. 10 in the context of a Stroop test. In this example, in stepS359 a [4 Khz, 8 Khz] BSRD is generated in step S359, and subsequentlythis BSRD (which is, in fact, a pulsatile BSRD) is converted into anon-pulsatile BSRD by filtering out pulsatile frequencies thereof usinga bandpass filter (step not shown in FIG. 10) to yield a non-pulsatileBSRD. This non-pulsatile BSRD corresponds to non-pulsatile physiologicaloscillations of blood shear in ‘pulsatile blood’ (see, for example, FIG.1B). At that point, in step S363, the following ‘target parameter P’ iscomputed −P=normalized energy of the portion of the non-pulsatile BSRDsignal in the [0.05 Hz, 0.15 Hz] range i.e. normalized relative to themagnitude of the non-pulsatile BSRD) of [0.05 Hz, 0.15 Hz] frequencies.

This was performed twice—once before the ‘Stroop test’ when the subjectwas in a relative ‘low-stress state’ and once ‘during the troop test’(i.e when the subject is in a higher stress state due to the mentaleffort of the Stropp test)—this was performed on 42 subjects times andgraphed in FIG. 11A is P(during test)-P(before test). As shown in FIG.11A, most of the time for the post-Stroop-test (or ‘stressed’ person)the value of P increased as a result of the Stroop test (thus P(duringtest)—P(before test) is usually positive), indicating that a greateramount of normalized energy of the non-pulsatile BSRD in the [0.05 Hz,0.15 Hz] frequency band is indicative of an ‘elevated’ mental stressstate.

FIG. 11B illustrates one example of the results of performing the methodof FIG. 10 in the context of detecting apnea events. In this example, instep S359 a [10 Khz, 24 Khz] BSRD is generated in step S359, andsubsequently this BSRD (which is, in fact, a pulsatile BSRD) isconverted into a non-pulsatile BSRD by filtering out pulsatilefrequencies thereof using a band-pass filter (step not shown in FIG. 10)to yield a non-pulsatile BSRD. This non-pulsatile BSRD corresponds tonon-pulsatile physiological oscillations of blood shear in ‘pulsatileblood’ (see, for example, FIG. 1B).

At that point, in step S363, the following target parameter P iscomputed—(ratio between (i) energy of the Mayer-frequency (i.e. in thefrequency-band [0.05 Hz, 0.15 Hz] components of the BSRD to (ii) energyof the non-pulsatile BSRD in the [0.15, 0.7 Hz] frequency band.

This was performed twice—once before the ‘Stroop test’ when the subjectwas in a relative ‘low-stress state’ and once ‘after the trooptest’—this was performed on 42 subjects times and graphed in FIG. 10A isthe delta P where P=normalized energy of the portion of thenon-pulsatile BSRD signal in the [0.05 Hz, 0.15 Hz] range. As shown inFIG. 11A, most of the time for the post-Stroop-test (or ‘stressed’person) the value of P increased as a result of the Stroop test,indicating that a greater amount of normalized energy of thenon-pulsatile BSRD in the [0.05 Hz, 0.15 Hz] range is indicative of an‘elevated’ mental stress state.

Description of FIGS. 12-14 (Computing Mood and/or Stress and/or Fitnessand/or Emotion by Dynamically Computing a BSRD)

It is noted that there are many ways to transform a scattered-lighttime-dependent optical response signal and a BSRD signal, depending onthe frequency selection profile. The biological meaning of a ‘frequencyselection profile’ may relate to a type of blood (e.g. within arteriesor capillaries, near the wall or near the centerline, etc) for which theBSRD signal is relevant. Thus, the optical response signal represents an‘ensemble’ of blood vessels (and an ‘ensemble’ of locations therein)—thefrequency selection profile for BSRD generation may relate to selectionof vessels of the ensemble or locations within these vessels.

When trying to sense stress and/or mood, the optimal BSRD and/orfrequency selection may vary between individuals or may vary for asingle individual over time. Use of a sub-optimal BSRD (or sub-optimalweighting) may yield fail to capture the prevailing biological status ofthe subject and thus result in an inaccurate detection.

For any scattered-light-optical-response descriptive electrical signal,there are many ways to transform the scattered-light-optical—responsedescriptive electrical signal into a BSRD—each transformation may beassociated with a different frequency-selection profile and would thusgenerate a different BSRD. In view of this relatively ‘large number’ ofpossible transformations, it is not always clear a priori whichtransformation will provide the most accurate prediction of a subject'sstress-state and/or mood-state and/or emotion-state cardiovascularfitness parameter. The best mood and/or stress and/or emotion and/orfitness-predictor for one mammalian subject may not necessarily be thebest for another subject—furthermore, the ‘best predictor’ may changeover time.

In the example of FIG. 12, BSRDs are scored relative to a ‘targetphysiological response signal’ (e.g. Mayer wave) provided in step S199in computer storage. As noted above, for the present disclosure, theterm ‘response signal’ therefore relates to the response(s) to inputand/or feedback from the central nervous system as manifested withinblood flow As noted above with reference to FIG. 2B, a pulsatile signalhas a known pre-determined signal form. Similarly, physiologicalresponse signals have known signal forms. This signal form may be in thetime domain or in the frequency domain—thus, in some embodiments, apower frequency spectrum may be stored in step S199.

More than one BSRD may be generated (steps S309-S131) and the BSRD's(e.g. non-pulsatile BSRDs) may be scored (step S317) according toprominence of a sub-Hz physiological signal therein. Candidate BSRDsignals are generated in step S309-S313, each candidate BSRD signal maybe ‘scored’ (see step S317) and the scores may be compared to each other(step S323).

FIG. 13 (discussed below) relates to an example technique for scoring a‘candidate BSRD’ according to prominence of a Mayer wave therein—thistechnique is one example of the scoring of step S317 of FIG. 12A and ofstep S367 of FIG. 12B.

In one example, the ‘target non-pulsatile physiological signal (i.e.selected in step S199) may be a one example is a Mayer wave). Thus, FIG.13 illustrates one example for how to ‘score’ a BSRD (e.g. non-pulsatileBSRD) in step S317 of FIG. 12.

In particular, FIG. 13 relates to the case where the ‘physiologicalresponse signal’ is a Mayer wave and a form of this signal in thefrequency form is analyzed. Four candidates are illustrated in FIG.13—each candidate represents a power spectrum of the BSRD and the arrowpoints to the ‘highest scoring’ candidate of the four candidate—i.e.having a power spectrum functional form that closest matches that of aMayer wave. As noted above, a Mayer wave is just one example of aresponse signal.

As illustrated in FIG. 12, in step S323 the better scoring BSRD may bepreferred (e.g. afforded a greater weight) to the lower scoring BSRDs.

FIG. 12 is one example of a ‘dynamic’ generation of BSRD. In oneexample, the ‘dynamic generation’ may relate to comparing multiplecandidates.

As noted above, it is not often clear a priori which transformationfunction yields the best results. Furthermore, even for the samesubject, the best-scoring transformation function may fluctuate intime—i.e. for an earlier time-period a first transformation functionyields the ‘highest score’ while for a later time-period a secondtransformation function yields the ‘highest score.’

Time periods may be defined according to time windows—see FIG. 14A whichillustrates overlapping time-windows and FIG. 14B which illustratesnon-overlapping time-windows.

FIG. 12B is an example of a method for processing a scattered-lighttime-dependent optical response signal into a BSRD (e.g. non-pulsatileBSRD) according to a dynamic and responsive technique which periodicallyupdates the transformation function (i.e. selected from a ‘family’ offunctions) in order to optimize a ‘score’ of the time-dependentblood-shear-rate descriptive signal where the ‘score’ describesprominence.

Thus, in step S361 after a time window is selected, instead of applyingonly a single transformation function for processing (i.e. for theparticular time window) the scattered-light time-dependent opticalresponse signal into a BSRD, it is possible to perform thetransformation a number of times—each time, the transformation isperformed using a different transformation function (i.e. associatedwith a different respective ‘frequency-selection profile). The resultsare scored in step S367—i.e. as discussed above with reference to stepS317 of FIG. 12A, candidate BSRDs where a prominence of thenon-pulsatile target signal (e.g. Mayer wave) within the candidate BSRDmay be assigned a greater score than candidate (e.g. the ‘best-scoring’time-dependent blood-shear-rate descriptive signal is employed whencomputing therefrom the mood and/or emotion and/or stress and/orcardiovascular fitness parameter).

The time window is updated in step S373. For each time window, the‘best’ transformation function may be different—therefore, thetransformation between scattered-light time-dependent optical responsesignal into a time-dependent blood-shear-rate descriptive signal is saidto be performed dynamically in response to scoring for presence and/orstrength of features of the non-pulsatile physiological signal of stepS199

FIG. 13 (discussed below) relates to an example technique for scoring a‘candidate BSRD’ according to prominence of a Mayer wave therein—thistechnique is one example of the scoring of step S317 of FIG. 12A and ofstep S367 of FIG. 12B.

The ‘Mayer wave’ is just one example of the non-pulsatile physiologicalsignal of step S199. Other examples may include a signal describing aneurogenic contribution to oscillations/fluctuations of blood shear inblood vessel(s) (or locations therein) and a respiratory contribution tooscillations/fluctuations of blood shear in blood vessel(s) (orlocations therein).

A Discussion of FIGS. 15A-15B, 16 and 17

As noted above, (i) BSRDs different from each other according tofrequency selection profile used to generate each BSRD from the signaldescriptive of the induced scattered-light time-dependent opticalresponse; and (ii) because of the many different possible frequencyselection profiles, there are fundamental differences between thedifferent BSRDs.

FIG. 15A relates to four categories of BSRDs: (i) a firstcategory—‘category A’ where a value of x is at most 300 Hz or at most275 Hz or at most 250 Hz or at most 200 Hz or at most 150 Hz or at most100 Hz) of [x Hz, ˜300 Hz] BSRDs; (ii) a second category—‘category B’ of[˜300 Hz, ˜1000 Hz] BSRDs (iii) a third category—category—‘category C’of [˜1000 Hz, ˜4000 Hz] BSRDs; and (iv) a fourthcategory—category—‘category D’ of [˜4000 Hz, z Hz] BSRDs where indifferent embodiments of z is at least 5000 or at least 6000 or at least7500.

Unless the BSRDs are post-processed to filter out pulsatile components,the category D BSRDs tend to be dominated by pulsatile components. Indifferent embodiments, category BSRDs tend to be descriptive of bloodsheer in arterial blood at locations distanced from the walls, whereblood tends to be pulsatile.

FIG. 15B relates to a ‘category E’ [y Hz, ˜150 Hz] BSRD where a value ofx is at most 100 Hz or at most 75 Hz or at most 50 Hz. In someembodiments, a ‘category E’ BSRD is analyzed. For the present disclosure˜150 Hz is at most 250 Hz or at most 200 Hz or at most 175 Hz and atleast 75 Hz or at least 100 Hz or a least 125 Hz.

In contrast, in some embodiments the low-frequency-dominated (andnon-pulsatile category A BSRD tend to be derived primarily from lightreflected off of slow-moving red blood-cells (RBCs) in endothelial bloodflow and/or at locations close to the walls. Category B BSRDs also tendto be non-pulsatile, though to a lesser extent than Category A BSRDs.With reference to FIG. 16, consider two category B BSRDs deride from thesame—a first category B BSRD having a frequency selection profile [α,β₁]at the top of FIG. 16 and a second category B BSRD having a frequencyselection profile [α,β₂] at the bottom of FIG. 16 where (i) α is ˜300Hz; (ii) both β₁ and β₂ are ˜1000 Hz, and (iii) β₁>β₂, for example,β₁-β₂>50 Hz. In this case, the energy of the ‘first’ category B BSRDexceeds that of the ‘second’ category B BSRD—however, the ‘first’category B BSRD is ‘more pulsatile’ than the ‘second’ category B BSRDbecause at frequencies of ˜1000 Hz as the frequency increases morepulsatile components are introduced into the BSRD.

Reference is made to FIG. 17. In some embodiments, it is possible todynamically generate (i.e. where the frequency profile is dynamicallyselected) a category BSRD signal B signal and to compute therefrom anemotion and/or mood and/or stress and/or cardiovascular fitnessparameter. Thus, in step S601 a category B BSRD signal having afrequency selection profile [α,γ] is generated and in step S609-S613 astrength of residual pulsatile contributions to the category B BSRDsignal is computed—for example, the contribution of a signal of FIG. 2Bhaving the pulsatile wave form to the category B BSRD signal having afrequency selection profile [α,γ]. If the contribution is relatively‘low’ then in step S623 it is possible to generate a category B BSRDhaving a higher value of γ—in this case, there is less of a ‘concern’about ‘noisy’ pulsatile components ‘polluting’ the category B BSRDsignal and it is preferable to generate a category B BSRD signal havinga greater total energy from which to derive the subject's motion and/oremotion and/or mood and/or fitness parameter. Conversely, if thecontribution is relatively ‘high’ (indicating a ‘noisy’ category Bsignal) then in step S619 it is possible to generate a category B BSRDhaving a lower value of γ to reduce the ‘puslatile noise’—in this case,because there is more of a ‘concern’ about ‘noisy’ pulsatile components‘polluting’ the category B BSRD signal, it is preferable to take stepsto reduce the ‘pulsatile noise’ even if the ‘price paid’ is a category BBSRD signal having a lesser total energy. The subject's motion and/oremotion and/or mood and/or fitness parameter is computed from the ‘lowergamma’ category B signal.

Similar to the method of FIG. 12, the method of FIG. 16 relates to thedynamic generation of a BSRD in accordance with analysis of BSRDfeature(s). Thus, the discussion above with reference to FIGS. 14A-14Bmay apply with respect to the method of FIG. 16 mutatis mutandis.

A Discussion of FIGS. 18A-18C

FIGS. 18A-18C relates to method whereby (i) a non-pulsatile BSRD issubjected, in step S425, to a stochastic analysis and/or an analysisquantifying a stationary/non-stationary nature of the non-pulsatileBSRD; and (ii) according to the results of the subjecting, emotionand/or mood and/or stress and/or cardiovascular fitness parameters (or aclassification) of a type of stress is computed in step S429.

For example, when the subject's stress-level changes this may modify thebalance between competing vasoconstrictors and vasodilators, yieldingstochastic behavior.

One example of such stochastic analysis is computing a fractal dimensionof the non-pulsatile BSRD signal. Another example is computing a Hurstexponent. In another example, an entropy of the non-pulsatile BSRDsignal is quantified.

In steps S401-S409 of FIG. 18A, this non-pulsatile BSRD is generated byscattering light off of red blood cells to generate a scattered-lighttime-dependent optical response (step S401-S405)—in step S409 thenon-pulsatile BSRD is computed directly or indirectly from thescattered-light time-dependent optical response.

For example, as illustrated in step S413 of FIG. 18B, the non-pulsatileBSRD may be computed by selecting the appropriate frequency-selectionprofile—e.g. [α,β] where β<=˜1000 Hz. Alternatively, in the example ofFIG. 18B, a pulsatile BSRD (e.g. a weakly pulsatile BSRD (e.g. a‘category C’ BSRD) is computed or a strongly pulsatile BSRD (e.g. a‘category C’ BSRD) is computed). In step S421, the pulsatile components(i.e. having a signal-form like that illustrated in FIG. 2B) arefiltered out of the pulsatile BSRD yielding the non-pulsatile BSRD

In some embodiments, one difference between the method of FIG. 18B andFIG. 18A are that they extract different types of non-pulsatile flowinformation—the method of FIG. 18B extract information fromnon-pulsatile blood (e.g. within capillaries or endothelial flow blood)while FIG. 18C extracts non-pulsatile information from pulsatile bloodby filtering out the pulsatile signal which ‘masks’ this non-pulsatileinformation.

FIG. 19 illustrates one example of the results of performing the methodof FIG. 18 in the context of a sound test. In this example, in step S409a [0 Khz, 400 Khz] BSRD is generated. In step S425, the following‘target parameter P’ is computed −P=Hurst exponent of the non-pulsatileBSRD signal generated in step S409. The experiment was the ‘soundtest’—i.e. subjecting the people of the test to an unpleasant sound andobserving the physiological consequences (i.e. within blood flow) of theensuing stress resulting from the unpleasant sound.

This computing of the ‘target parameter P’ was performed twice—oncebefore the ‘sound test’ when the subject was in a relative ‘low-stressstate’ and once ‘during the sound test’ (i.e when the subject was in ahigher stress state due to)—this test was performed 135 times where the.FIG. 19 illustrates a histogram of the results where the ‘lighter bars’represent ‘relaxed states’ and the darker bars represent the stressedstate (i.e. after the subjects hear the unpleasant and stress-inducingsound). As shown in the graph, the parameter P can differentiate betweenthe ‘relaxed’ and ‘sound-stressed’ individuals.

FIG. 20 illustrates one example of the results of performing the methodof FIG. 18 in the context of a cardiovascular fitness test. In thisexample, in step S409 a [400 Khz, 400 Khz] BSRD is generated. In stepS425, the following ‘target parameter P’ is computed −P=Hurst exponent(chaos indication) of the non-pulsatile BSRD signal generated in stepS409 of the person in the ‘stating state’ minus Hurst exponent (chaosindication) of the non-pulsatile BSRD signal generated in step S409 ofthe person in the ‘supine state’. The orthostatic test’ experiment(cardiovascular fitness parameter) was—steps S409 and S425 wereperformed for a signal generated when the subject was supine (i.e. stepS401-S405 were performed when the subject was supine) and again when thesubject was standing.

FIG. 20A is a histogram of the results (test performed 43 times one perperson)—most of the time the Hurst coefficient increases. FIG. 20B is ahistogram of other results (test performed 22 times one per person)—mostof the time the Hurst coefficient increases. In FIG. 20B P before testis the Hurst exponent for when the subject was supine and P after thetest is the Hurst exponent for when the subject was standing.

FIGS. 21A-21B illustrate another example of a parameter generatedaccording to FIG. 18—the tests are performed for two groups ofsubjects—one group of ‘trained’ or physically fit subjects (in thecircle) and another group for has a chronic problem. Each subject had aunique number (on the x axis) and a particular parameter (on the y axis)was computed in step S425. Each result is thus its own data point—theresults are graphed in FIGS. 21A-21B.

Discussion of FIGS. 22-25

In the discussion above with reference to FIG. 15, four types of BSRDswere discussed: (i) extremely non-pulsatile category A BSRDs; (ii)non-pulsatile category B BSRDs which may include very weak pulsatilecomponents; (iii) weakly pulsatile category C BSRDs and (iv) stronglypulsatile category D BSRDs.

Referring to FIG. 22A, it is noted that embodiments of the presentinvention relate to situations where (i) BSRDs from two or three or fourof these categories are generated (step S509) and each analyzed (stepS525) and (ii) according to the results of this analysis (step S529)emotion and/or mood and/or stress and/or cardiovascular fitness iscomputed.

In some embodiments, a different respective classifier/predictor (i.e.for computing emotion and/or stress and/or cardiovascular fitness) maybe introduced.

In theory, it may be possible to generate a single BSRD having afrequency profile that includes the profiles of two or more of theBSRDs. However, when this information is mixed together it may in factbe ‘noise’—in contrast, it is possible to (i) ‘separate’ thisinformation by generating separate BSRDs and then (ii) recombine thisinformation. A separate predictor/classifier (i.e. for determiningemotion and/or mood and/or stress and/or cardiovascular fitness and/or a‘type’ of stress (e.g. mental versus emotional) may be provided for eachBSRD category. Each BSRD-category-specific predictor/classifier may beemployed to combine a classification/prediction of emotion and/or moodand/or stress and/or cardiovascular fitness and/or a ‘type’ of stressand the results may be combined to provide an accuracy-boosted combinedclassifier/predictor.

Any method of combining multiple predictors/classifiers may be employedincluding but limited to Markov models, multiple regression, baggingalgorithms, and voting techniques.

FIG. 22B shows data flow related to the method of FIG. 22A.

The weighing between the different categories of BSRD may be static or,in some embodiments, may be dynamic. In one example related to FIG. 23,a classifier may be based upon pulsatile BSRD. In this example, it maybe possible to (i) generate a pulsatile BSRD in step S651 (ii) analyzethe pulsatile BSRD (e.g. category D BSRD) in step S665-S769 to determinea prominence of pulsatile features therein—i.e. the determine if therewas a ‘good measurement’ of pulse. For example, in the presence ofmotion artifacts or other physical perturbations may preclude a goodmeasurement of pulse and reduce the pulsatile quality/prominence ofpulsatile features in the pulsatile BSRD

In the event that there was a ‘good pulse measurement’ (step S665) theweight of the pulsatile BSRD signal (e.g. category D BSRD) may bedynamically increased at the expense of the weight of the non-pulsatileBSRD (e.g. category A or B BSRD). Conversely, in the event that therewas a ‘poor pulse measurement’ (step S669) the weight of the pulsatileBSRD signal (e.g. category D BSRD) may be dynamically decreased, whilecommensurately increasing a weight of the non-pulsatile BSRD (e.g.category A or B BSRD).

FIG. 23 relates to the example of weighing between different BSRD inorder to increase an accuracy of measurement of emotion and/or stress(or type of stress) and/or cardiovascular parameter. FIG. 24 relate todynamic generation of BSRDs for the same purpose. In particular, FIG. 24relates to the situation wherein multiple BSRDs are generated, the‘quality’ of a first BSRDs is scored (e.g. pulsatile BSRD scored insteps S655-S659) and a second BSRD is generated according to a frequencyselection profile determined in response to the results of scoring ofthe first BSRD.

Thus, in the example of FIG. 24: a pulsatile BSRD is generated andscored (step S651-S659)—for example, a strong pulsatile BSRD and/orcategory D BSRD. In the event that the prominent pulsatile features aredetected within the pulsatile BSRD, this may be indicative of aprominence of pulsatile information even at frequencies near the ‘lowerfrequency range’ where pulsatile information may be found. In this case,there may be a concern of ‘polluting’ the category B BSRD with pulsatileinformation. Thus, in response to detecting these ‘strong’ pulsatilefeatures in a first BSRD (i.e. pulsatile), the frequency ceiling for asecond BSRD (i.e. category B BSRD) may be reduced in step S669, even ifthe ‘price paid’ for this is a lower energy category B BSRD. Conversely,in situations where the pulsatile BSRD has less prominent pulsatilefeatures, this is less of a concern. In response to detecting thissituation (step S673), it may be decided to dynamically increase the˜100 Hz frequency ‘ceiling’ for the Category B BSRD.

As shown in flow diagram of FIG. 25, the generation of a particular typeof BSRD may be in accordance with (i) downstream feedback fromgenerating the BSRD (see for example, FIG. 12 where the BSRD is analyzedfor a prominence of non-pulsatile features) and (ii) feedback fromgenerating even a different type of BSRD (see, for example, FIG. 24).

In addition, as shown in FIG. 23, the weighting between differentcategories of BSRDs may be dynamic.

A Discussion of FIG. 26A-26H—Analyzing Multiple Types of BSRDs (Fitness)

As shown in FIGS. 26A-26D, by combining data from multiple types ofBSRDs, it is possible to more accurately predict/detect stress and/oremotion and/or fitness. FIGS. 26A-26D relate to fitness. For each ofFIG. 26A-26D, generation and analysis of a BSRD is performed for twogroups of subjects—one group of ‘trained’ or physically fit subjects(lighter data points) and another group of ‘not fit’ subjects (darkerdata points). Each subject had a unique number (on the x axis) and aparticular parameter computed by analyzing a particular type of BSRD wascomputed.

In FIG. 26A, a category A BSRFD was generated [0 Hz, 300 Hz] and thiscategory A BSRD was subjected to an analysis such that a fraction of theenergy in the band [0.15 Hz, 0.7 Hz] (respiration band) was computed(i.e. relative contribution). The ‘prediction power’ is p=0.0044 where alower number is a better predictor.

In FIG. 26B a category B BSRD was generated [300 Hz, 1 KHz] wasgenerated and this category B BSRD was subjected to an analysis suchthat a fraction of the energy in the band [0.15 Hz, 0.7 Hz] (respirationband) was computed (i.e. relative contribution). The prediction power is0.02—thus, the parameter of FIG. 26A is a better predictor.

In FIG. 26C a [4 Khz, 10 Khz] pulsatile BSRD was generated, pulsatilecomponents were removed (to obtain therefrom a non-pulsatile BSRD), andthis non-puslatile BSRD was analyzed such that a fraction of the energyin the band [0.05 Hz, 0.15 Hz] was computed (i.e. relativecontribution)˜the prediction power is 0.008—thus, the parameter of FIG.26A is a better predictor while the parameter of FIG. 26B is a worsepredictor.

In FIG. 26D predictive/measurement power of a combined index ispresented—→−0.7+1.1*(parameter of FIG. 26A)+1.15*(parameter of FIG.26B). This combined index has a greater predictive power than theindices of any of FIGS. 26A-26C. In particular, a value of p is 0.00045.

In FIG. 26E predictive/measurement power of a combined index ispresented—→−0.95+1.23*(parameter of FIG. 26A)+1.45*(parameter of FIG.26C). This combined index has a greater predictive power than theindices of any of FIGS. 26A-26D—in particular, a value of p is 0.00008.

FIGS. 26A-26E related to measuring cardiovascular fitness. FIGS. 26F-26Grelate to measuring stress.

In FIG. 26F, a [0 KHz, 300 KHz] BSRD was generated—this category Anon-BSRD was subjected to a stochastic analysis to compute a Hurstcomponent (see FIGS. 18A-18B).

In FIG. 26G, a [2 KHz, 8 KHz] BSRD was generated and pulsatilecomponents were removed to generate a non-pulsatile BSRD—thinon-pulsatile BSRD s was subjected to an analysis such that a fractionof the energy in the band [0.05 Hz, 0.15 Hz] (respiration band) wascomputed (i.e. relative contribution).

FIG. 26H relates to combining the analyses of the different BSRDs—i.e.combining FIGS. 26F-26G to increase measurement/prediction power ofstress.

Thus, as discussed above (see FIG. 22A) it is possible to improvemeasurement accuracy by generating multiple ‘types’ of BSRDs, analyzingeach BSRD, and combining the results—this may be performed for fitness(FIGS. 26A-26E), stress (FIGS. 26F-26H), mood, apnea or any other‘physiological measurement target’ disclosed herein.

A Discussion of FIG. 26I-26L—Analyzing Multiple Types of BSRDs (Stress)

As shown in FIGS. 26A-26D, by combining data from multiple types ofBSRDs, it is possible to more accurately predict/detect stress and/oremotion and/or fitness. FIGS. 26A-26D relate to stress.

FIG. 26I relates to a respiratory index for bandpass 500 Hz-1 KHz. FIG.26J relates to a myogenic index for a bandpass 1-3 KHz. FIG. 26K relatesto a Hurst index for bandpass 0-500 Hkz.

FIG. 26 L relates to a combined index—Combined WeighedIndex=0.44-3.44*(HURST INDEX)+1.3*(Myogenic Index)+1.87*(RespiratoryIndex)

The predictive power (i.e. to distinguish between a ‘stressed group’ anda ‘normal group’) of the combined index (FIG. 26L) is stronger than theother indices.

A Discussion of FIG. 27

As discussed above, a Mayer wave is only one type of physiologicalresponse signal. As discussed above (see FIGS. 10 and 12) the ‘targetsignal’ may be expressed in either the time domain or the frequencydomain (i.e. as a power spectrum). Similarly, any ‘scoring’ of a BSRDmay entail scoring a power spectrum of the BSRD with reference to apower spectrum of a target physiological response signal.

FIGS. 27A-27D relate to four ‘baseline’ BSRDs—all generated when asingle subject is in a ‘relaxed state’ FIGS. 28A-28D relate to four‘baseline’ BSRDs—all generated when the same single subject is‘listening to emotional music’ which changes his mood-state. FIGS.29A-29D relate to four ‘baseline’ BSRDs—all generated when the samesingle subject is performing mental exercise—i.e. a mental stress-load.FIGS. 30A-30D relate to four ‘baseline’ BSRDs—all generated after thesingle subject hears an unpleasant sound.

FIGS. 27A, 28A, 29A and 30A all illustrate a power spectrum of a [0 Khz,0.4 KHz] BSRD (i.e. after filtering out pulsatile components using abandpass filter thus for a [0 Khz, 0.4 Khz] BSRD this is not necessary)Thus, it is possible to compare the power spectrum of FIG. 28A to thatof FIG. 27A to detect the influence of ‘emotional music’ on the [0 Khz,0.4 KHz] BSRD. It is possible to compare the power spectrum of FIG. 29Ato that of FIG. 27A to detect the influence of ‘mental load/stress’ onthe [0 Khz, 0.4 KHz] BSRD. It is possible to compare the power spectrumof FIG. 30A to that of FIG. 27A to detect the influence of ‘unpleasantsound’ on the [0 Khz, 0.4 KHz] BSRD.

FIGS. 27B, 28B, 29B and 30B all illustrate a power spectrum of a [0.4Khz, 1 KHz] BSRD (i.e. after filtering out pulsatile components using abandpass filter thus for a [0.4 Khz, 1 KHz] BSRD this is not necessary)Thus, it is possible to compare the power spectrum of FIG. 28B to thatof FIG. 27B to detect the influence of ‘emotional music’ on the [0.4Khz, 1 KHz] BSRD. It is possible to compare the power spectrum of FIG.29B to that of FIG. 27B to detect the influence of ‘mental load/stress’on the [0.4 Khz, 1 KHz] BSRD. It is possible to compare the powerspectrum of FIG. 30B to that of FIG. 27B to detect the influence of‘unpleasant sound’ on the [0.4 Khz, 1 KHz] BSRD.

FIGS. 27C, 28C, 29C and 30C all illustrate a power spectrum of a [1 KHZ,4 KHZ] BSRD (i.e. after filtering out pulsatile components using abandpass filter thus for a [1 KHZ, 4 KHZ] BSRD this is not necessary)Thus, it is possible to compare the power spectrum of FIG. 28C to thatof FIG. 27C to detect the influence of ‘emotional music’ on the [1 KHZ,4 KHZ] BSRD. It is possible to compare the power spectrum of FIG. 29C tothat of FIG. 27C to detect the influence of ‘mental load/stress’ on the[1 KHZ, 4 KHZ] BSRD. It is possible to compare the power spectrum ofFIG. 30C to that of FIG. 27C to detect the influence of ‘unpleasantsound’ on the [1 KHZ, 4 KHZ] BSRD.

FIGS. 27D, 28D, 29D and 30D all illustrate a power spectrum of a [4 KHZ,10 KHZ] BSRD (i.e. after filtering out pulsatile components using abandpass filter thus for a [4 KHZ, 10 KHZ] BSRD this is not necessary)Thus, it is possible to compare the power spectrum of FIG. 28D to thatof FIG. 27D to detect the influence of ‘emotional music’ on the [4 KHZ,10 KHZ] BSRD. It is possible to compare the power spectrum of FIG. 29Dto that of FIG. 27D to detect the influence of ‘mental load/stress’ onthe [4 KHZ, 10 KHZ] BSRD. It is possible to compare the power spectrumof FIG. 30D to that of FIG. 27D to detect the influence of ‘unpleasantsound’ on the [4 KHZ, 10 KHZ] BSRD.

First Additional Discussion of Embodiments

A method for optically measuring, according to one or more a stressand/or mood and/or stress-resistance cardiovascular fitness parameterspecific to a warm-blooded subject, the method comprising: a.illuminating a portion of the subject's skin or tissue by a VCSEL(vertical cavity surface emitting laser) or a diode laser to scatterpartially or entirely coherent light off of the subject's moving redblood cells (RBCs) to induce a scattered-light time-dependent opticalresponse; b. receiving the scattered light by a photodetector(s) togenerate an electrical signal descriptive of the induced scattered-lighttime-dependent optical response; c. processing thescattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom one or moreblood-shear-rate-descriptive (BSRD) signal(s), each BSRD signalcharacterized by a respective frequency-selection profile; d.electronically analyzing features of the BSRD signal(s) of the BSRDsignal group; e. in accordance with the results of the electronicallyanalyzing of the at least two frequency-interval-specificshear-rate-descriptive signals, performing at least one of the followingof subject-status-classification operation(s): (i) classifying astress-state (e.g. type of stress or level of stress) of the subject;(ii) classifying a mood-state of the subject; (iii) classify astress-resistance of the subject; (iv) classifying a cardiovascularfitness-status of the subject. wherein a frequency-selection profile ofthe BSRD(s) signal is computed dynamically so to adaptively maximize aprominence of a predetermined non-pulsatile physiological signal withinthe BSRD(s) and/or wherein the classification operation is performeddynamically so that a weight assigned to a BSRD signal is adaptivelydetermined to increase a weight of BSRD signal(s) whosefrequency-selection profile correspond to a greater prominence of thepredetermined non-pulsatile physiological signal at the weight-expenseof BSRD signal(s) whose frequency-selection profile correspond to alesser prominence of the predetermined non-pulsatile physiologicalsignal.

In some embodiments, the predetermined non-pulsatile physiologicalsignal is a Mayer wave signal.

A method for optically measuring, according to one or more a stressand/or mood and/or stress-resistance cardiovascular fitness parameterspecific to a warm-blooded subject, the method comprising: a.illuminating a portion of the subject's skin or tissue by a VCSEL(vertical cavity surface emitting laser) or a diode laser to scatterpartially or entirely coherent light off of the subject's moving redblood cells (RBCs) to induce a scattered-light time-dependent opticalresponse; b. receiving the scattered light by a photodetector(s) togenerate an electrical signal descriptive of the induced scattered-lighttime-dependent optical response c. processing thescattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom a non-pulsatileblood-shear-rate-descriptive (BSRD) signal(s), each BSRD signalcharacterized by a respective frequency-selection profile; d. subjectingthe non-pulsatile BSRD signal(s) to a stochastic analysis or to astationary-status analysis that quantifies a stationary/non-stationarystatus of the BSRD signal(s); e. in accordance with the results of thestochastic and/or stationary-status analysis, performing at least one ofthe following of subject-status-classification operation(s): (i)classifying a stress-state (e.g. type of stress or level of stress) ofthe subject; (ii) classifying a mood-state of the subject; (iii)classify a stress-resistance of the subject; (iv) classifying acardiovascular fitness-status of the subject.

In some embodiments, non-pulsatile BSRD signal(s) are dynamicallycomputed such that the frequency-selection profile thereof isdynamically adjusted so as to maximize a signal energy while minimizinga residual-pulse component of the BSRD signal(s).

In some embodiments, the method is performed adaptively such that: i.one or more non-pulsatile candidate BSRD signal(s) are scored so that(A) a greater signal energy and a lower pulsatile signal-contributionincrease a quality-score of a rated non-pulsatile candidate BSRD signaland (B) conversely, a lower signal energy and a greater pulsatilesignal-contribution decrease a quality-score of a rated non-pulsatilecandidate BSRD signal; and ii. the subject-status-classificationoperation is performed dynamically so as to assign greater weight tocandidate BSRD signal(s) having a higher score and to assign a lowerweight to candidate BSRD signal(s) having a lower score.

In some embodiments, i. a pulsatile BSRD signal(s) is also generatedfrom the scattered-light-optical-response-descriptive electrical signalor derived signal thereof; ii. subject-status-classificationoperation(s) is performed according to both feature(s) of the pulsatileBSRD signal(s) and the results of the stochastic and/orstationary-status analysis of the non-pulsatile BSRD signal(s); iii. thepulsatile BSRD signal(s) is rated according to a prominence ofblood-pressure-waveform feature(s) therein; and iv. the non-pulsatileBSRD signal(s) is dynamically computed such that the frequency-selectionprofile thereof is dynamically adjusted.

A method for optically measuring, according to one or more a stressand/or mood and/or stress-resistance cardiovascular fitness parameterspecific to a warm-blooded subject, the method comprising: a.illuminating a portion of the subject's skin or tissue by a VCSEL(vertical cavity surface emitting laser) or a diode laser to scatterpartially or entirely coherent light off of the subject's moving redblood cells (RBCs) to induce a scattered-light time-dependent opticalresponse; b. receiving the scattered light by a photodetector(s) togenerate an electrical signal descriptive of the induced scattered-lighttime-dependent optical response or an AC component thereof; c.processing the scattered-light-optical-response-descriptive electricalsignal or a derived-signal thereof to compute therefrom at least twoblood-shear-rate-descriptive (BSRD) signal(s) selected from the BSRDsignal group, each blood-rate-descriptive BSRD signal characterized by adifferent respective frequency-selection profile, the BSRD signal groupconsisting of the following signals: (i) a [sub-200 Hz, ˜300 Hz] BSRDsignal; (ii) a [˜300 Hz, ˜1000 Hz] signal; (iii) a [˜1000 Hz, ˜4000 Hz]signal and (iv) a [˜4000 Hz, z Hz] (z>=7,000) signal; d. electronicallyanalyzing features at least two the BSRD signals of the BSRD signalgroup; e. in accordance with the results of the electronically analyzingof the at least two frequency-interval-specific shear-rate-descriptivesignals, performing at least one of the following ofsubject-status-classification operation(s): (i) classifying astress-state (e.g. type of stress or level of stress) of the subject;(ii) classifying a mood-state of the subject; (iii) classify astress-resistance of the subject; (iv) classifying a cardiovascularfitness-status of the subject.

A machine-learning-based method for optically measuring, according toone or more a stress and/or mood and/or stress-resistance cardiovascularfitness parameter specific to a warm-blooded subject, the methodcomprising: a. monitoring behavior patterns of the subject by cameraand/or receiving data via a graphical-user-interface and/or monitoringinteractions of the user with advertisement(s) and/or according to audiooutput of the user; b. illuminating a portion of the subject's skin ortissue by a VCSEL (vertical cavity surface emitting laser) or a diodelaser to scatter partially or entirely coherent light off of thesubject's moving red blood cells (RBCs) to induce a scattered-lighttime-dependent optical response; c. receiving the scattered light by aphotodetector(s) to generate an electrical signal descriptive of theinduced scattered-light time-dependent optical response d. processingthe scattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom one or moreblood-shear-rate-descriptive (BSRD) signal(s), each BSRD signalcharacterized by a respective frequency-selection profile; e. inaccordance with a correlation between (i) a result of the monitoring ofthe subject's behavior patterns of step (a) and (ii) feature(s) of theBSRD signal(s), training a subject-status-classifier capable ofclassifying a subject-status, in accordance with BSRD-signal-derivedinput, at least one a stress-state (e.g. type of stress or level ofstress) a mood-state, a stress-resistance, and a cardiovascularfitness-status of the subject; f. at a later time, employing the trainedclassifier to perform at least one of the following ofsubject-status-classification operation(s) according to later BSRDsignal data: (i) classifying a stress-state (e.g. type of stress orlevel of stress) of the subject; (ii) classifying a mood-state of thesubject; (iii) classify a stress-resistance of the subject; (iv)classifying a cardiovascular fitness-status of the subject.

In some embodiments, the classifying of a stress-state comprisesdistinguishing between any two of mental-stress, emotional-stress and/ordetermining if a dominant stress mode of the subject is physical,emotional or mental.

In some embodiments, the classifying of a stress-state comprisesquantifying an extent of stress and/or the classifying of thestress-resistance comprises classifying a stress-resistance-level of thesubject.

In some embodiments, further comprising according to thesubject-status-classification operation, (i) triggering at least one ofan alert and therapy and/or (ii) serving advertisement to a user and/or(iii) updating the subject's user-profile and/or (iv) adjustingdisplay-parameter(s) of a GUI operated by the user, wherein at least oneof step(s) c-e is/are performed using a processor.

Apparatus for optically obtaining state and/or status information orchanges therein about a warm-blooded subject the apparatus comprising:

-   -   a. a diode laser or VCSEL configured to illuminate the subject's        skin so as to scatter partially or entirely coherent light off        of moving red blood cells (RBCs) of the subject to induce a        scattered-light time-dependent optical response;    -   b. photodetector(s) configured to generate an electrical signal        descriptive of the induced scattered-light time-dependent        optical response; and    -   c. electronic circuitry configured to perform any method        disclosed herein.

Second Additional Discussion

It is widely recognized that effective stress management could have adramatic impact on health care and preventive medicine. In order to meetthis need, efficient and seamless sensing and analytic tools for thenon-invasive stress monitoring during daily life are required. Theexisting sensors still do not meet the needs in terms of specificity androbustness. We utilized a miniaturized dynamic light scattering sensor(mDLS) which is specially adjusted to measure skin blood flowfluctuations and provides multi-parametric capabilities. Based on themeasured dynamic light scattering signal from the red blood cellsflowing in skin, a new concept of hemodynamic indexes (HI) andoscillatory hemodynamic indexes (OHI) have been developed. This approachwas utilized for stress level assessment for a few use-case scenario.The new stress index was generated through the HI and OHI parameters. Inorder to validate this new non-invasive stress index, a group of 19healthy volunteers was studied by measuring the mDLS sensor located onthe wrist. Mental stress was induced by using the cognitive dissonancetest of Stroop. We found that OHIs indexes have high sensitivity to themental stress response for most of the tested subjects. In addition, weexamined the capability of using this new stress index for theindividual monitoring of the diurnal stress level. We found that the newstress index exhibits similar trends as reported for to the well-knowndiurnal behavior of cortisol levels. Finally, we demonstrated that thisnew marker provides good sensitivity and specificity to the stressresponse to sound and musical emotional arousal.

Self-monitoring and ability to recognize and keep track of our ownhealth and wellness has become possible with the growing capability ofwearable sensors to generate data about our bodies. One of the mostimportant parameters of health and wellness is the stress level. Whilein the short term, a certain amount of stress is essential for normalhealth, with chronic stress, those same responses can suppress functionsthat are not required for immediate survival. Numerous emotional andphysical disorders have been linked to the chronic stress. One of theman increased risk of hypertension. In addition, an excessive level ofmental stress in daily life, perceived stress during working hours andjob stress has been considered as a risk factor for cardiovascular andanxiety disorders One of the great challenges for successful stressmanagement is determining what causes the stress and how to quantify it.Thus, a capability to measure stress level variation continuously can bea key factor for the proper management of different stressors in ourdaily life.

Concerning stress monitoring, several questions should be addressed. Thefirst one is how to express the physiological characteristics in termsof the measured data. The second question is how to convert thesecharacteristics into specific quantitative physiological features.

A method and apparatus for quantification of stress level is disclosed.This quantification may be obtained by analyzing of the laser specklesresponses to the skin blood flow dynamics. This information is used forthe determination of the blood flow oscillatory characteristics. Forthis end, we introduced additional hemodynamic parameters that can bederived from the laser speckle signals. We called them the HemodynamicIndexes (HI) and Oscillatory Hemodynamic Indexes (OHI). Thesecharacteristics are directly related to manifestations of the autonomicnervous system (ANS) and cardiovascular system (CVS) responses and couldbe used as complementary information to already existing non-invasivemarkers of stress.

Physiological Parameters of Stress—

The autonomic nervous system (ANS) regulates most of the physiologicalactivity of our body, including heart rate, blood pressure, peripheralblood flow and more. Parasympathetic (PSMP) and sympathetic (SMP)activities are part of ANS. Multiple processes regulate this system.Auto-regulatory mechanisms and hormones circulating in blood directlyinfluence cardiovascular function by affecting the rate and strokevolume of the heart and the contraction or dilatation of blood vessels.Thus, peripheral blood hemodynamics exhibit many features underlyingneural and cardio-vascular physiology. During stress events, the SMP isresponsible for fast activation of the system and the PSMP is associatedwith relaxation. Eventually, real-life stress conditions produce changesin autonomic cardiac and vascular regulation.

It is commonly accepted fact that physiological rhythms affect nearlyall body functions including PSMP and SMP. The ANS and endocrine signalsare the principal mediators of this process. The level of stress,therefore, is also governed by these rhythms. This fact has beendemonstrated by measuring significant daily variations including plasmaconcentrations of cortisol and other hormones.

Non-Invasive Markers of Stress—

Since the heart rate response to stressors is mediated by the ANS,variations of heart rate is a marker of parasympathetic or sympatheticactivity. Quantitative analysis of HR activity is commonly performed byanalyzing the fluctuation pattern of the heart rhymes. The durationbetween two consecutive R waves of the electrocardiogram (ECG) aredefined as RR intervals. The variation of RR intervals or HRV (heartrate variability) is beat-to-beat alterations in heart rate. HRV is usedas a function of sympatho-vagal balance of our body, which is closelyrelated to the stress status. The most accurate HRV are measured byusing ECG sensors. As an alternative methodology, PPG signal is used forthe measurement of pulse-to-pulse variations. The waveform analysis ofthe PPG signal enables to determine peaks of the systolic wave and thepulse rate, and the HRV parameters can be approximately calculated.

However, the quantification of ANS functioning through the HRVcharacteristics is not always reliable. This is a result of the factthat physiological systems are comprised of multiple subsystems thatexhibit a variety of regulation processes, operating over multiple timescales and conditions. Therefore, most of the measured characteristicsare driven by very complex dynamics and more information is required todescribe it.

Another well-known marker of stress is GSR (galvanic skin response). GSRis mediated mainly through the sympathetic nerve supply to the skin, andit is entirely attributable to changes in the sweat glands. One oftechnical disadvantages of GSR is that external factors such astemperature and humidity affect GSR measurements, and can lead toinconsistent results. In addition, GSR is sensitive mainly to thesympathetic responses and very important parasympathetic functioning isless reflected in the GSR signal.

Blood Flow Oscillations

One of the most important physiological characteristics of our body isthe peripheral microcirculation of skin blood flow (SBF). The skinmicrocirculation is governed by arterioles, capillaries, and venules.SBF is regulated by centrally mediated neural mechanisms and by localhumoral factors. Both rhythmic and stochastic changes in blood flow aregoverned, therefore, by CVS, neural and metabolic processes. Theseoscillations can be used as a source of information related to neuralactivity. The peripheral microcirculation or SBF is commonly studiedthrough the laser Doppler flowmetry (LDF) technique.

Power spectrum analyses of LDF signals reveal a few distinct frequencieswithin the range of 0.01-2 Hz: the spectral component around 1 Hzcorresponds to the cardiac activity. The other spectral components inthe lowest frequency bands represent the influence of the respiration(0.3 Hz), myogenic activity or vasomotion (0.1 Hz) and neurogenicactivity (0.04 Hz). The very specific oscillation appearing in the0.05-0.15 Hz is frequently associated with so-called Mayer waves.

Several important studies addressing physiological interpretation of LDFfluctuations for stress monitoring have been published. For example,Goor et al. demonstrated that peripheral arterial vasoconstrictionpredicts stress-induced myocardial ischemia. They described that acutemental stress will lead to sympathetic nervous system activation andconsequent peripheral vasoconstriction.

A variety of analytic tools for analysis and interpretation of bloodflow fluctuations have been developed to date. These include frequencydomain methods based on the Fourier transform, wavelet analysis, fractalanalysis, singular spectrum analysis (SSA), multiscale entropy algorithmand more. The majority of the important results in processing andanalysis of physiological signals consider the signals consisting ofmulti-periodic components mixed with random noise.

However, it has to be taken into consideration that the measured SBFsignal is a convolution of many independent sources. Different vesselsand events in different parts of the vessels including small arteries,arterioles and capillary vessels contribute independently andconcurrently into the measured signal. Therefore, presenting SBF as asingle variable which is a subject for oscillatory analysis is notsufficient for the comprehensive interpretation of the physiologicalactivity.

By using a new kind of sensor (mDLS) and a new algorithmic approach, wedeveloped a methodology for the signal decomposition into differentcomponents associated with different hemodynamic sources. This approachcan be used for multidimensional analysis of the ANS and CVSmanifestations. In this work, we demonstrated the usability of this newapproach for assessment of the stress level.

Dynamic Light Scattering Sensor for the Measurement of Skin Blood Flow:Sensor Design:

The miniaturized dynamic light scattering sensor (mDLS of Elfi-Tech)enables measurement of the laser speckle signals originated by the skinblood flow. The mDLS sensor consists of the VCSEL chip which is closelylocated between two photodetectors (FIG. 42A), analog front end and dataacquisition unit.

The very small distance between the detectors and the light sourceenables suppression of the multiple scattering effects of the reflectedlight. Only the photons that have been directly backscattered from thered blood cells are detected. The analog subtraction of two measuredsignals efficiently rejects the correlated components of the measuredsignal while uncorrelated DLS component is enhanced following thesubtraction process. The number of laser speckles appearing on thephotodetector determines speckle statistics. Presumably, the singlebackscattering events mainly are responsible for the measured signal.However, forward single scattering component might be involved in theoverall signal. Indeed, thanks to the intensive scattering by the tissue(immobile “lattice” of the connective tissue) a significant number ofphotons are redirected to the backward hemisphere while these photonsare actually scattered by the RBC's in forward direction¹⁹. Thus, inaddition to the backscattered light, significant proportion of forwardscattering light also detected. It should be noted that immobilescatterers or scatters that move with the same uniform velocity does notaffect the temporal pattern of the measured signal.

Theoretical Discussion: Shear-Rate Model of the Flow

The relative movement of RBC's particles in the blood vessels is definedby a velocity profile of blood flow. In a very simplified case, for thevessel of radius R, axis symmetric velocity profiles v(r,t) can bedescribed in cylindrical coordinates by this empirical relationship:

$\begin{matrix}{{{v\left( {r,t} \right)} = {{{{{v(0)}\left\lbrack {1 - \left( \frac{r}{R} \right)^{\xi}} \right\rbrack}{f(t)}} - R} \leq r \leq R}};} & (1)\end{matrix}$

Where v(0)—is maximum velocity at the center position r=0 and R is theradius of the vessel, f(t) is a periodic function of heart beatfrequency, which is driven by difference between systolic and diastolicpressure wave and it is time phase-shifted with respect to the cardiaccycle, and represents the degree of blunting. For example, in 30 micronarterioles, there is a range of ξ=2.4-4 at normal flow rates. If ξ=2, aparabolic velocity distribution is obtained (see FIG. 2 which showsBlood flow profile in the vessels).

One of the most important rheological parameters is velocity shear rateγ. It is given by:

$\begin{matrix}{{\gamma = {\frac{\partial{v\left( {r,t} \right)}}{\partial r} = {\xi \cdot {v\left( {o,t} \right)} \cdot \frac{r^{\xi - 1}}{R\;\xi}}}},} & (2) \\{{v\left( {o,t} \right)} = {\frac{\xi + 2}{\xi} < {v(t)}}} & (3)\end{matrix}$

Where (v)—the velocity averaged over the cross-sectional area.

The rheological term “shear rate” is almost synonymous with velocitygradient. Shear rate is determined by the diameter of vessels. In bloodvessels, the shear rate is not purely parabolic because of theNon-Newtonian rheological behaviors of the flowing blood. Thenon-Newtonian behavior of blood is due to the tendency of erythrocytesto aggregate at low shear rates. The highest shear rate is achieved whenflow is fast and vessel diameter is small, and lowest shear rate ispresent when flow is slow and the vessel has a large diameter.

For small arterioles (from 15-to 60 microns diameter), the fluctuationof velocity from systolic to diastolic phases ranges from 1.5 mm/s to2.5 mm/s, where mean velocity is around 10 mm/sec. The shear rate forsmall arterioles is between 400 (l/sec) to 1400 (l/sec). For thecapillaries from 5-10 microns, where an average velocity is around 0.2mm/sec the shear rate can range from 50 to 100 (l/sec). Therefore, weare in the region where shear rate is sufficiently high to alter theparticles space configuration before it can relax by the Brownianmotion.

Theoretical Discussion: 1.1 Dynamic Light Scattering and Shear Rate

The measured signal can be expressed in terms of the dynamic lightscattering (DLS) formalism. This formalism considers a relative movementof the scatterers as a major source of the laser speckles dynamics. Whenan ensemble of moving particles creates the scattering pattern on thedetector, only the particles that are spatially correlated have to betaken into consideration. The particles separated by large distancesgive negligible contribution into the autocorrelation function or powerspectrum of the signal. This relative movement of these closely spacedparticles is the only characteristics that is preserved after theensemble averaging.

It was shown that for the laminar flow the autocorrelation function g(τ)of measured DLS is dependent on the gradient of the velocity:ΛV(r)=V(x,r)−V(x,r+Λr)   (4)

Approximately, in laminar blood flow¹⁸, the characteristic decay time ofautocorrelation function can be given by:g _(i) ^(f)(τ)∝exp(−Γ_(i) ^(f)τ²)   (5)Γ^(f) _(i)=(γ_(i) ·<d<·q)²   (6)

where q=2 k·sin(θ/2), θ—is scattering angle, k is wavelength number and<d> is the effective distance across the scattering volume in thedirection of the velocity gradient. Superscript f signifies the relationto flow and subscript i is assigned to specific shear rate value.

It has to be pointed out, that for the shear rate model, theautocorrelation function of the signal decays with a time squiredependence rather than the simple exponential time dependence, which isthe typical description for the Brownian motion.g _(i) ^(f)(τ)∝exp(−Γ_(i) ^(f)τ²)   (7)Γ_(i) ^(B) =D _(i) ·q ²   (8)

D₁—diffusion coefficient for red blood cells. Subscript B relates to theBrownian motion.

It has to be taken into consideration that the speckle signals arecontributed by a variety of shear rates. The shear rates distributioncan be associated with different types of the blood vessels or differentregions inside the vessels. The lowest shear rate values correspond tothe RBCs located mostly near the walls or flowing through the narrowcapillary blood vessels and their decay function is dominated by theBrownian movement statistics. The very short decay time is associatedwith the large capillary vessels or arterioles.

We approximate the autocorrelation function G of the amplitudefluctuation as the weighed sum (W_(i)) of all speckle components withdifferent time constants:

$\begin{matrix}{{G(\tau)} = {{\sum\limits_{i}{w_{i}^{B} \cdot g_{i}^{B}}} + {\sum\limits_{i}{w_{i}^{f} \cdot g_{i}^{f}}}}} & (9)\end{matrix}$

According to Wiener-Khintchine theorem we can express the result interms of the power spectrum:P(ω)=FT(G(τ))   (10)

Where FT—is Fourier transform. After substituting G(τ) from (7) and (8)we have:

$\begin{matrix}{{P(\omega)} = {{{{FT}\left( {\sum\limits_{i}{w_{i}^{f} \cdot g_{i}^{f}}} \right)} + {{FT}\left( {\sum\limits_{i}{w_{i}^{B} \cdot g_{i}^{B}}} \right)}} = {{\sum\limits_{i}{w_{i}^{f} \cdot {P_{i}^{G}(\omega)}}} + {\sum\limits_{i}{w_{i}^{B} \cdot {P_{i}^{L}(\omega)}}}}}} & (11)\end{matrix}$

Where:

$\begin{matrix}{{P(\omega)} = {{\sum\limits_{i}{w_{i}^{f}\frac{\exp\left( {{\omega^{2}/4}\Gamma_{i}^{f}} \right)}{2 \cdot \sqrt{\Gamma_{i}^{f}}}}} + {\sum\limits_{i}{w_{i}^{B}\frac{2 \cdot \Gamma_{i}^{B}}{\left( \Gamma_{i}^{B} \right)^{2} + \omega^{2}}}}}} & (12)\end{matrix}$

Thus, the resulting spectrum is approximated by a superposition of twocomponents: the Gaussian P_(Γ)(ω) and the Lorentzian P_(L)(ω).

As we have shown, the temporal statistics of the DLS signal may reflectthe complex behavior reflecting neural functioning that are expressedthrough the peripheral skin blood circulation.

Hemodynamic Indexes

Söderström et al showed that for an ensemble of particles moving withdifferent velocities, the Doppler spectrum can be decomposed bydifferent velocities. Liebert et al⁴ showed that by decomposing the SBFsignal measured from the skin, different oscillatory patterns arerevealed.

In order to facilitate the interpretation of an oscillatory analysis, weintroduced a so-called hemodynamic index HI.

When the measured signal is expressed in terms of power spectrum P, wedefine hemodynamic index HI by:

$\begin{matrix}{{{HI}\left( {\left\lbrack {f_{1},f_{2}} \right\rbrack,t} \right)} = {\int_{f_{1}}^{f_{2}}{{P\left( {\omega,t} \right)}\ d\;\omega}}} & (13)\end{matrix}$

where [f₁,f₂]=2Pi*[w1,w2] defines the bandpass.

HI is defined by a specific bandpass and corresponds to a certain rangeof shear rates. Physiologically, each HI signifies different sorts ofblood vessels or different regions in the vessels. For example, HI(t)that exhibits a pulsatile pattern resembling the blood pressure wave isassociated with the arterioles. HI values which is associated with thecapillary blood exhibits oscillatory behavior that differs fromarteriole component of HI(t).

Based on (13) by using (11) and (12) we can easily get for HI(ω₁, ω₂)the following:

$\begin{matrix}{{HI} \propto {{{Erf}\left( \frac{\omega_{2}}{2\sqrt{\left\langle \Gamma^{f} \right\rangle}} \right)} - {{Erf}\left( \frac{\omega_{1}}{2\sqrt{\left\langle \Gamma^{f} \right\rangle}} \right)} + \frac{{2\left\langle \Gamma^{B} \right\rangle} + {{Arc}\;{\tan\left( \frac{\omega_{2}}{\left\langle \Gamma^{B} \right\rangle} \right)}} - {{Arc}\;{\tan\left( \frac{\omega_{1}}{\left\langle \Gamma^{B} \right\rangle} \right)}}}{\left\langle \Gamma^{B} \right\rangle}}} & (14)\end{matrix}$

Where <Γ^(B)> and <Γ^(f)> are representing an average shear rate andBrownian related constants in autocorrelation functions for each shearrate component.

In order to estimate Γ_(i) ^(f)=(γ_(i)·<d>_(i)·q)² for capillary blood,for example, we can take γ_(i)≈20 sec⁻¹, q_(i)=2·π·n/λ(backscattering:180°), λ=0.8μ. where <d>_(i) is defined as the distanceacross the scattering volume in the direction of the velocity gradient.

On FIG. 3 we can see an example of behavior of two different HI's. HI1is defined by the low shear rate of 10 sec⁻¹ for capillary blood and HI2is calculated for 50 sec⁻¹ plotted as function of mean bandpassesfrequency. The f2−f1 for each bandpass was chosen 1 KHZ.

FIG. 33 shows HI for as function of the frequencies

In this example it is seen that specific HI1 is entirely defined under acur-off frequency of 4 KHZ. Under 1 Khz the Brownian component has to betaken into consideration.

We can interpret the HI dependence on the shear rate by rendering toeach bandpass a corresponding effective velocity or shear rates values.Differentiation between the shear rates is closely related to the typeof the blood, like capillary, arterial, endothelial etc.

The HI that is related to very low frequency range addresses theendothelial interaction with RBC's⁷ where the high frequency region ischaracterized mostly the pulsatile blood flow. The oscillatorycharacteristics are served as an additional measure that has to beperformed for each HI. Following the calculation of a set of HIvariables we can carry out different types of oscillatory analysis foreach of them. To simplify our analysis we used a discrete physiologicaloscillation filters bank. For example, in this study we used thefollowing bands; [0.005, 0.05] Hz—endothelial related band, defined as(E), ([0.15] Hz—myogenic wave region (M), [0.15, 0.6]—Respiratory (R),[0.6, 3] Hz, Pulsatile (P). The corresponding normalized power spectrumcomponent of HI over the measurement interval T are defined as OHI(oscillatory HI components), so for each HI we can select a number ofoscillatory components.

Altogether, this full physiological pattern is expressed throughso-called OHI matrix, which incorporates information about thetime-dependent behavior of different shear rates being represented bydifferent HI's. If we use n frequency (f) bandpass intervals then we get

$\begin{matrix}{{OHI} = \begin{Bmatrix}\begin{matrix}{{{OHI}\left( {\left\lbrack {f_{1},{f_{1} + {\nabla f}}} \right\rbrack,E} \right)},{{OHI}\left( {\left\lbrack {f_{1},{f_{1} + {\nabla f}}} \right\rbrack,M} \right)},} \\{{{OHI}\left( {\left\lbrack {f_{1},{f_{1} + {\nabla f}}} \right\rbrack,R} \right)},{{OHI}\left( {\left\lbrack {f_{1},{f_{1} + {\nabla f}}} \right\rbrack,P} \right)}}\end{matrix} \\\ldots \\\begin{matrix}{{{OHI}\left( {\left\lbrack {f_{n},{f_{n} + {\nabla f}}} \right\rbrack,E} \right)},{{OHI}\left( {\left\lbrack {f_{n},{f_{n} + {\nabla f}}} \right\rbrack,M} \right)},} \\{{{OHI}\left( {\left\lbrack {f_{n},{f_{n} + {\nabla f}}} \right\rbrack,R} \right)},{{OHI}\left( {\left\lbrack {f_{n},{f_{n} + {\nabla f}}} \right\rbrack,P} \right)}}\end{matrix}\end{Bmatrix}} & (15)\end{matrix}$

Generally, this matrix can be expended by introducing the additionalnon-deterministic characteristics and variables of the fluctuations,like fractal dimensions, Hurst exponents and more.

The evolution of OHI matrix in time can be represented inmultidimensional space as a trajectory of physiological status. Togetherwith heart rate and HRV, the dynamics of OHI matrix parameters reflectsvariety of cardio-vascular and neurological processes.

Physiological Manifestations of the Hemodynamic Indexes—

The mDLS signals where collected while a subject was sitting comfortablyin a chair. The sensor was attached to the upper side of the wrist.

FIGS. 34a and 34b each exemplify two different OHI components extractedfrom the mDLS signal. On FIG. 34A we show HI ([7.5 KHZ, 30 KHZ], clearlycorresponding to the pulsatile blood pressure waveform. On FIG. 34B thetime dependency pattern for HI ([0, 1 KHZ) for the same measurementinterval is presented. The fluctuations of this HI variable are relatedmainly to the capillary blood.

FIG. 34A shows HI for the pulsatile component; FIG. 34B shows HI for thenon-pulsatile component;

In other examples (FIG. 35A and FIG. 35B) the so-call Mayer oscillationis seen (around 0.1 Hz). The 0.1 pulsatile component for HI(8 KHZ, 30Hz, t) is modulated by the 0.1 Hz oscillation. FIG. 34B shows that thenon-pulsatile component HI([1-3 KHZ] is modulated by 0.1 Hz as well.FIG. 35A shows a Mayer wave in pusalite HI. FIG. 35B shows Mayer wave innon-pusalite HI.

Different HI(t) reflect, therefore, different physiological patternsthat can be expressed in terms of oscillation analysis. Examples of theOscillation patterns for different HI in power spectrum graphs are shownon FIGS. 6a and 6b

FIG. 6a . OHI for Pusalite Component

FIG. 6b . OHI of Non-Pusalite Component.

Recently the usefulness of Hemodynamic Indexes was demonstrated in ananimal study¹³. In this study HI's behavior tested for postoperativeevaluation of anastomotic microcirculation. It was shown that only HIcorresponding to the low shear rate (non-pulsatile) and can be used forthe detection of anastomotic leakage in colorectal surgery. In order tostudy usability of OHI matrix for assessment of stress response wecreated an experimental set up when the examined subject is exposed tophysiological stimulus.

PCT/IB2015/001157, filed on May 21, 2015, is incorporated herein byreference. Any combination of any feature described in the presentdocument and any feature or combination of feature(s) described inPCT/IB2015/001157 is within the scope of the invention.

The present invention has been described using detailed descriptions ofembodiments thereof that are provided by way of example and are notintended to limit the scope of the invention. The described embodimentscomprise different features, not all of which are required in allembodiments of the invention. Some embodiments of the present inventionutilize only some of the features or possible combinations of thefeatures. Variations of embodiments of the present invention that aredescribed and embodiments of the present invention comprising differentcombinations of features noted in the described embodiments will occurto persons of the art.

What is claimed is:
 1. A method for optically measuring state and/orstatus information or changes therein about a warm-blooded subject, themethod comprising: a. illuminating a portion of the subject's skin ortissue by a VCSEL (vertical cavity surface emitting laser) or a diodelaser to scatter partially or entirely coherent light off of thesubject's moving red blood cells (RBCs) to induce a scattered-lighttime-dependent optical response; b. receiving the scattered light by aphotodetector(s) to generate an electrical signal descriptive of theinduced scattered-light time-dependent optical response; c. processingthe scattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom one or moreblood-shear-rate-descriptive (BSRD) signal(s), each BSRD signalcharacterized by a respective frequency-selection profile; d.electronically analyzing features of the BSRD signal(s) of the BSRDsignal group; e. in accordance with the results of the electronicallyanalyzing of the frequency-interval-specific shear-rate-descriptivesignal(s), computing the state and/or status information or changestherein from the results of the analyzing; wherein: i. afrequency-selection profile of the BSRD(s) signal is computeddynamically so as to adaptively maximize a prominence of a predeterminednon-pulsatile physiological signal within the BSRD(s); and/or ii.computation of the state and/or status information is performeddynamically so that a weight assigned to a BSRD signal is adaptivelydetermined to increase a weight of BSRD signal(s) whosefrequency-selection profile correspond to a greater prominence of thepredetermined non-pulsatile physiological signal at the weight-expenseof BSRD signal(s) whose frequency-selection profile correspond to alesser prominence of the predetermined non-pulsatile physiologicalsignal.
 2. The method of claim 1 wherein the measured state is aneurological state.
 3. The method of claim 1 wherein the measured stateis a fitness state.
 4. The method of claim 1 wherein state and/or statusinformation comprises at least one of: a stress-state, acardiovascular-fitness, a pain-state, a fatigue-state, astress-resistance, a diurnal fluctuation of stress or stress-resistance,and an apnea event.
 5. The method claim 1 wherein the predeterminednon-pulsatile physiological signal is a Mayer wave signal.
 6. The methodclaim 1 wherein the predetermined non-pulsatile physiological signal isa neurogenic signal.
 7. The method of claim 1 wherein the predeterminednon-pulsatile physiological signal is a myogenic signal.
 8. The methodclaim 1 wherein the predetermined non-pulsatile physiological signal isa respiratory signal.
 9. A method for optically measuring state and/orstatus information or changes therein about a warm-blooded subject, themethod comprising: a. illuminating a portion of the subject's skin ortissue by a VCSEL (vertical cavity surface emitting laser) or a diodelaser to scatter partially or entirely coherent light off of thesubject's moving red blood cells (RBCs) to induce a scattered-lighttime-dependent optical response; b. receiving the scattered light by aphotodetector(s) to generate an electrical signal descriptive of theinduced scattered-light time-dependent optical response c. processingthe scattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom a non-pulsatileblood-shear-rate-descriptive (BSRD) signal(s), each BSRD signalcharacterized by a respective frequency-selection profile; d. subjectingthe non-pulsatile BSRD signal(s) to a stochastic analysis or to astationary-status analysis that quantifies a stationary/non-stationarystatus of the BSRD signal(s); e. computing the state and/or statusinformation or changes therein from the results of the stochastic and/orstationary-status analysis.
 10. The method of claim 9 wherein thestochastic and/or stationary-status analysis comprises computing atleast one of: a fractal dimension of the BSRD signal(s), an entropy ofthe BSRD signal(s), and a Hurst component of the BSRD signal(s).
 11. Themethod of claim 9 wherein the stochastic and/or stationary-statusanalysis comprises computing at least one of: a fractal dimension of theBSRD signal(s), an entropy of the BSRD signal(s), and a Hurst componentof the BSRD signal(s).
 12. Apparatus for optically measuring stateand/or status information or changes therein about a warm-bloodedsubject the apparatus comprising: a. a diode laser or VCSEL configuredto illuminate the subject's skin so as to scatter partially or entirelycoherent light off of moving red blood cells (RBCs) of the subject toinduce a scattered-light time-dependent optical response; b.photodetector(s) configured to generate an electrical signal descriptiveof the induced scattered-light time-dependent optical response; and c.electronic circuitry configured to perform the following: i. processingthe scattered-light-optical-response-descriptive electrical signal or aderived-signal thereof to compute therefrom at least two or at leastthree or at least four blood-shear-rate-descriptive (BSRD) signalsselected from the BSRD signal group, each blood-rate-descriptive BSRDsignal characterized by a different respective frequency-selectionprofile, the BSRD signal group consisting of the following signals: (i)a [sub-200 Hz, ˜300 Hz] BSRD signal; (ii) a [˜300 Hz, ˜1000 Hz] BSRDsignal; (iii) a [˜1000 Hz, ˜4000 Hz] BSRD signal and (iv) a [˜4000 Hz, zHz] (z>=7,000) BSRD signal; ii. electronically analyzing features of theat least two or at least 3 or at least 4 BSRD signals of the BSRD signalgroup; iii. in accordance with the results of the electronicallyanalyzing of the at least two or at least 3 or at least 4 BSRD signals,computing the state and/or status information or changes therein. 13.Apparatus of claim 12 wherein the measured state is a neurologicalstate.
 14. Apparatus of claim 12 wherein the measured state is a fitnessstate.
 15. Apparatus of claim 12 wherein state and/or status informationcomprises at least one of: a stress-state, a cardiovascular-fitness, apain-state, a fatigue-state, a stress-resistance, a diurnal fluctuationof stress or stress-resistance, and an apnea event.